Parameter monitoring compensation system and method

DOEpatents

Barkman, William E.; Babelay, Edwin F.; DeMint, Paul D.; Hebble, Thomas L.; Igou, Richard E.; Williams, Richard R.; Klages, Edward J.; Rasnick, William H.

1995-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along preprogrammed path during a machining operation utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer.

Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Toliyat, Hamid A.

2005-01-01

An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Parameter monitoring compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F.; DeMint, P.D.; Hebble, T.L.; Igou, R.E.; Williams, R.R.; Klages, E.J.; Rasnick, W.H.

1995-02-07

A compensation system is described for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation. It utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer. 7 figs.

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

NASA Astrophysics Data System (ADS)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data

DTIC Science & Technology

2014-10-02

condition monitoring , and equipment time to failure prediction in manufacturing 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 589...Component Level Health Monitoring and Prediction One of the characteristics of a self-aware machine is to be able to detect its components...the annual conference of the prognostics and health management society. Filzmoser, P., Garrett, R. G., & Reimann, C . (2005). Mul- tivariate outlier

Application of TRIZ approach to machine vibration condition monitoring problems

NASA Astrophysics Data System (ADS)

Cempel, Czesław

2013-12-01

Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

Condition monitoring of Electric Components

NASA Astrophysics Data System (ADS)

Zaman, Ishtiaque

A universal non-intrusive model of a flexible antenna array is presented in this paper to monitor and identify the failures in electric machines. This adjustable antenna is designed to serve the purpose of condition monitoring of a vast range of electrical components including Induction Motor (IM), Printed Circuit Board (PCB), Synchronous Reluctance Motor (SRM), Permanent Magnet Synchronous Machine (PMSM) etc. by capturing the low frequency magnetic field radiated around these machines. The basic design and specification of the proposed antenna array for low frequency components is portrayed first. The design of the antenna is adjustable to fit for an extensive variety of segments. Subsequent to distinguishing the design and specifications of the antenna, the ideal area of the most delicate stray field has been identified for healthy current streaming around the machineries. Following this, short circuit representing faulty situation has been introduced and compared with the healthy cases. Precision has been found recognizing the faults using this one generic model of Antenna and the results are presented for three different machines i.e. IM, SRM and PMSM. Finite element method has been used to design the antenna and detect the optimum location and faults in the machines. Finally, a 3D Printer is proposed to be employed to build the antenna as per the details tended to in this paper contingent upon the power segments.

On-line Monitoring for Cutting Tool Wear Condition Based on the Parameters

NASA Astrophysics Data System (ADS)

Han, Fenghua; Xie, Feng

2017-07-01

In the process of cutting tools, it is very important to monitor the working state of the tools. On the basis of acceleration signal acquisition under the constant speed, time domain and frequency domain analysis of relevant indicators monitor the online of tool wear condition. The analysis results show that the method can effectively judge the tool wear condition in the process of machining. It has certain application value.

A review on prognostic techniques for non-stationary and non-linear rotating systems

NASA Astrophysics Data System (ADS)

Kan, Man Shan; Tan, Andy C. C.; Mathew, Joseph

2015-10-01

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

PubMed

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

2015-03-25

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

PubMed Central

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Farhan Khan, Muhammad; Naeem, Muhammad; Anpalagan, Alagan

2015-01-01

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed. PMID:25815444

Research on intelligent monitoring technology of machining process

NASA Astrophysics Data System (ADS)

Wang, Taiyong; Meng, Changhong; Zhao, Guoli

1995-08-01

Based upon research on sound and vibration characteristics of tool condition, we explore the multigrade monitoring system which takes single-chip microcomputers as the core hardware. By using the specially designed pickup true signal devices, we can more effectively do the intelligent multigrade monitoring and forecasting, and furthermore, we can build the tool condition models adaptively. This is the key problem in FMS, CIMS, and even the IMS.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

PubMed Central

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-01-01

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. PMID:29690641

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

PubMed

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-04-23

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

PubMed Central

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-01-01

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514

Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

PubMed

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-11-14

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

[Extension of cardiac monitoring function by used of ordinary ECG machine].

PubMed

Chen, Zhencheng; Jiang, Yong; Ni, Lili; Wang, Hongyan

2002-06-01

This paper deals with a portable monitor system on liquid crystal display (LCD) based on this available ordinary ECG machine, which is low power and suitable for China's specific condition. Apart from developing the overall scheme of the system, this paper also has completed the design of the hardware and the software. The 80c196 single chip microcomputer is taken as the central microprocessor and real time electrocardiac single is data treated and analyzed in the system. With the performance of ordinary monitor, this machine also possesses the following functions: five types of arrhythmia analysis, alarm, freeze, and record of automatic pappering, convenient in carrying, with alternate-current (AC) or direct-current (DC) powered. The hardware circuit is simplified and the software structure is optimized in this paper. Multiple low power designs and LCD unit design are adopted and completed in it. Popular in usage, low in cost price, the portable monitor system will have a valuable influence on China's monitor system field.

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2013 CFR

2013-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2014 CFR

2014-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2012 CFR

2012-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2011 CFR

2011-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring.

PubMed

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-02-23

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

PubMed Central

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-01-01

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies. PMID:26907297

A hybrid approach for nondestructive assessment and design optimisation and testing of in-service machinery

NASA Astrophysics Data System (ADS)

Rahman, Abdul Ghaffar Abdul; Noroozi, Siamak; Dupac, Mihai; Mahathir Syed Mohd Al-Attas, Syed; Vinney, John E.

2013-03-01

Complex rotating machinery requires regular condition monitoring inspections to assess their running conditions and their structural integrity to prevent catastrophic failures. Machine failures can be divided into two categories. First is the wear and tear during operation, they range from bearing defects, gear damage, misalignment, imbalance or mechanical looseness, for which simple condition-based maintenance techniques can easily detect the root cause and trigger remedial action process. The second factor in machine failure is caused by the inherent design faults that usually happened due to many reasons such as improper installation, poor servicing, bad workmanship and structural dynamics design deficiency. In fact, individual machines components are generally dynamically well designed and rigorously tested. However, when these machines are assembled on sight and linked together, their dynamic characteristics will change causing unexpected behaviour of the system. Since nondestructive evaluation provides an excellent alternative to the classical monitoring and proved attractive due to the possibility of performing reliable assessments of all types of machinery, the novel dynamic design verification procedure - based on the combination of in-service operation deflection shape measurement, experimental modal analysis and iterative inverse finite element analysis - proposed here allows quick identification of structural weakness, and helps to provide and verify the solutions.

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

Bayesian anomaly detection in monitoring data applying relevance vector machine

NASA Astrophysics Data System (ADS)

Saito, Tomoo

2011-04-01

A method for automatically classifying the monitoring data into two categories, normal and anomaly, is developed in order to remove anomalous data included in the enormous amount of monitoring data, applying the relevance vector machine (RVM) to a probabilistic discriminative model with basis functions and their weight parameters whose posterior PDF (probabilistic density function) conditional on the learning data set is given by Bayes' theorem. The proposed framework is applied to actual monitoring data sets containing some anomalous data collected at two buildings in Tokyo, Japan, which shows that the trained models discriminate anomalous data from normal data very clearly, giving high probabilities of being normal to normal data and low probabilities of being normal to anomalous data.

Angular approach combined to mechanical model for tool breakage detection by eddy current sensors

NASA Astrophysics Data System (ADS)

Ritou, M.; Garnier, S.; Furet, B.; Hascoet, J. Y.

2014-02-01

The paper presents a new complete approach for Tool Condition Monitoring (TCM) in milling. The aim is the early detection of small damages so that catastrophic tool failures are prevented. A versatile in-process monitoring system is introduced for reliability concerns. The tool condition is determined by estimates of the radial eccentricity of the teeth. An adequate criterion is proposed combining mechanical model of milling and angular approach.Then, a new solution is proposed for the estimate of cutting force using eddy current sensors implemented close to spindle nose. Signals are analysed in the angular domain, notably by synchronous averaging technique. Phase shifts induced by changes of machining direction are compensated. Results are compared with cutting forces measured with a dynamometer table.The proposed method is implemented in an industrial case of pocket machining operation. One of the cutting edges has been slightly damaged during the machining, as shown by a direct measurement of the tool. A control chart is established with the estimates of cutter eccentricity obtained during the machining from the eddy current sensors signals. Efficiency and reliability of the method is demonstrated by a successful detection of the damage.

Design of a cardiac monitor in terms of parameters of QRS complex.

PubMed

Chen, Zhen-cheng; Ni, Li-li; Su, Ke-ping; Wang, Hong-yan; Jiang, Da-zong

2002-08-01

Objective. To design a portable cardiac monitor system based on the available ordinary ECG machine and works on the basis of QRS parameters. Method. The 80196 single chip microcomputer was used as the central microprocessor and real time electrocardiac signal was collected and analyzed [correction of analysized] in the system. Result. Apart from the performance of an ordinary monitor, this machine possesses also the following functions: arrhythmia analysis, HRV analysis, alarm, freeze, and record of automatic papering. Convenient in carrying, the system is powered by AC or DC sources. Stability, low power and low cost are emphasized in the hardware design; and modularization method is applied in software design. Conclusion. Popular in usage and low cost made the portable monitor system suitable for use under simple conditions.

Using Amazon Web Services (AWS) to enable real-time, remote sensing of biophysical and anthropogenic conditions in green infrastructure systems in Philadelphia, an ultra-urban application of the Internet of Things (IoT)

NASA Astrophysics Data System (ADS)

Montalto, F. A.; Yu, Z.; Soldner, K.; Israel, A.; Fritch, M.; Kim, Y.; White, S.

2017-12-01

Urban stormwater utilities are increasingly using decentralized "green" infrastructure (GI) systems to capture stormwater and achieve compliance with regulations. Because environmental conditions, and design varies by GSI facility, monitoring of GSI systems under a range of conditions is essential. Conventional monitoring efforts can be costly because in-field data logging requires intense data transmission rates. The Internet of Things (IoT) can be used to more cost-effectively collect, store, and publish GSI monitoring data. Using 3G mobile networks, a cloud-based database was built on an Amazon Web Services (AWS) EC2 virtual machine to store and publish data collected with environmental sensors deployed in the field. This database can store multi-dimensional time series data, as well as photos and other observations logged by citizen scientists through a public engagement mobile app through a new Application Programming Interface (API). Also on the AWS EC2 virtual machine, a real-time QAQC flagging algorithm was developed to validate the sensor data streams.

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

An illustration of new methods in machine condition monitoring, Part I: stochastic resonance

NASA Astrophysics Data System (ADS)

Worden, K.; Antoniadou, I.; Marchesiello, S.; Mba, C.; Garibaldi, L.

2017-05-01

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.

Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

PubMed Central

Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

2016-01-01

Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

NASA Astrophysics Data System (ADS)

Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

2018-03-01

Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

NASA Astrophysics Data System (ADS)

Zhou, Zu-De; Gui, Lin; Tan, Yue-Gang; Liu, Ming-Yao; Liu, Yi; Li, Rui-Ya

2017-09-01

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures introducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature monitoring technology and thermal deformation monitoring technology. A new optical measurement technology called the "fiber Bragg grating (FBG) distributed sensing technology" for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles to guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.

PubMed

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-05-31

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.

Online Vibration Monitoring of a Water Pump Machine to Detect Its Malfunction Components Based on Artificial Neural Network

NASA Astrophysics Data System (ADS)

Rahmawati, P.; Prajitno, P.

2018-04-01

Vibration monitoring is a measurement instrument used to identify, predict, and prevent failures in machine instruments[6]. This is very needed in the industrial applications, cause any problem with the equipment or plant translates into economical loss and they are mostly monitored component off-line[2]. In this research, a system has been developed to detect the malfunction of the components of Shimizu PS-128BT water pump machine, such as capacitor, bearing and impeller by online measurements. The malfunction components are detected by taking vibration data using a Micro-Electro-Mechanical System(MEMS)-based accelerometer that are acquired by using Raspberry Pi microcomputer and then the data are converted into the form of Relative Power Ratio(RPR). In this form the signal acquired from different components conditions have different patterns. The collected RPR used as the base of classification process for recognizing the damage components of the water pump that are conducted by Artificial Neural Network(ANN). Finally, the damage test result will be sent via text message using GSM module that are connected to Raspberry Pi microcomputer. The results, with several measurement readings, with each reading in 10 minutes duration for each different component conditions, all cases yield 100% of accuracies while in the case of defective capacitor yields 90% of accuracy.

A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring

NASA Astrophysics Data System (ADS)

Zhu, Xiaoliang; Zhong, Chong; Zhe, Jiang

2017-07-01

Detecting wear debris and measuring the increasing number of wear debris in lubrication oil can indicate abnormal machine wear well ahead of machine failure, and thus are indispensable for online machine health monitoring. A portable wear debris sensor with ferrite cores for online monitoring is presented. The sensor detects wear debris by measuring the inductance change of two planar coils wound around a pair of ferrite cores that make the magnetic flux denser and more uniform in the sensing channel, thereby improving the sensitivity of the sensor. Static testing results showed this wear debris sensor is capable of detecting 11 µm and 50 µm ferrous debris in 1 mm and 7 mm diameter fluidic pipes, respectively; such a high sensitivity has not been achieved before. Furthermore, a synchronized sampling method was also applied to reduce the data size and realize real-time data processing. Dynamic testing results demonstrated that the sensor is capable of detecting wear debris in real time with a high throughput of 750 ml min-1 the measured debris concentration is in good agreement with the actual concentration.

The monitoring of transient regimes on machine tools based on speed, acceleration and active electric power absorbed by motors

NASA Astrophysics Data System (ADS)

Horodinca, M.

2016-08-01

This paper intend to propose some new results related with computer aided monitoring of transient regimes on machine-tools based on the evolution of active electrical power absorbed by the electric motor used to drive the main kinematic chains and the evolution of rotational speed and acceleration of the main shaft. The active power is calculated in numerical format using the evolution of instantaneous voltage and current delivered by electrical power system to the electric motor. The rotational speed and acceleration of the main shaft are calculated based on the signal delivered by a sensor. Three real-time analogic signals are acquired with a very simple computer assisted setup which contains a voltage transformer, a current transformer, an AC generator as rotational speed sensor, a data acquisition system and a personal computer. The data processing and analysis was done using Matlab software. Some different transient regimes were investigated; several important conclusions related with the advantages of this monitoring technique were formulated. Many others features of the experimental setup are also available: to supervise the mechanical loading of machine-tools during cutting processes or for diagnosis of machine-tools condition by active electrical power signal analysis in frequency domain.

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

NASA Astrophysics Data System (ADS)

Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

2012-05-01

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Metallic wear debris sensors: promising developments in failure prevention for wind turbine gearsets and similar components

NASA Astrophysics Data System (ADS)

Poley, Jack; Dines, Michael

2011-04-01

Wind turbines are frequently located in remote, hard-to-reach locations, making it difficult to apply traditional oil analysis sampling of the machine's critical gearset at timely intervals. Metal detection sensors are excellent candidates for sensors designed to monitor machine condition in vivo. Remotely sited components, such as wind turbines, therefore, can be comfortably monitored from a distance. Online sensor technology has come of age with products now capable of identifying onset of wear in time to avoid or mitigate failure. Online oil analysis is now viable, and can be integrated with onsite testing to vet sensor alarms, as well as traditional oil analysis, as furnished by offsite laboratories. Controlled laboratory research data were gathered from tests conducted on a typical wind turbine gearbox, wherein total ferrous particle measurement and metallic particle counting were employed and monitored. The results were then compared with a physical inspection for wear experienced by the gearset. The efficacy of results discussed herein strongly suggests the viability of metallic wear debris sensors in today's wind turbine gearsets, as correlation between sensor data and machine trauma were very good. By extension, similar components and settings would also seem amenable to wear particle sensor monitoring. To our knowledge no experiments such as described herein, have previously been conducted and published.

Portable water quality monitoring system

NASA Astrophysics Data System (ADS)

Nizar, N. B.; Ong, N. R.; Aziz, M. H. A.; Alcain, J. B.; Haimi, W. M. W. N.; Sauli, Z.

2017-09-01

Portable water quality monitoring system was a developed system that tested varied samples of water by using different sensors and provided the specific readings to the user via short message service (SMS) based on the conditions of the water itself. In this water quality monitoring system, the processing part was based on a microcontroller instead of Lead and Copper Rule (LCR) machines to receive the results. By using four main sensors, this system obtained the readings based on the detection of the sensors, respectively. Therefore, users can receive the readings through SMS because there was a connection between Arduino Uno and GSM Module. This system was designed to be portable so that it would be convenient for users to carry it anywhere and everywhere they wanted to since the processor used is smaller in size compared to the LCR machines. It was also developed to ease the user to monitor and control the water quality. However, the ranges of the sensors' detection still a limitation in this study.

Urban land use monitoring from computer-implemented processing of airborne multispectral data

NASA Technical Reports Server (NTRS)

Todd, W. J.; Mausel, P. W.; Baumgardner, M. F.

1976-01-01

Machine processing techniques were applied to multispectral data obtained from airborne scanners at an elevation of 600 meters over central Indianapolis in August, 1972. Computer analysis of these spectral data indicate that roads (two types), roof tops (three types), dense grass (two types), sparse grass (two types), trees, bare soil, and water (two types) can be accurately identified. Using computers, it is possible to determine land uses from analysis of type, size, shape, and spatial associations of earth surface images identified from multispectral data. Land use data developed through machine processing techniques can be programmed to monitor land use changes, simulate land use conditions, and provide impact statistics that are required to analyze stresses placed on spatial systems.

Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

NASA Astrophysics Data System (ADS)

Fachrurrozi, Muhammad; Saparudin; Erwin

2017-04-01

Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.

A Method of High Throughput Monitoring Crop Physiology Using Chlorophyll Fluorescence and Multispectral Imaging.

PubMed

Wang, Heng; Qian, Xiangjie; Zhang, Lan; Xu, Sailong; Li, Haifeng; Xia, Xiaojian; Dai, Liankui; Xu, Liang; Yu, Jingquan; Liu, Xu

2018-01-01

We present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively. We choose chlorophyll fluorescence parameters and relative reflectance of multispectral as the indicators of crop physiological status. Using tomato as experiment subject, the typical crop physiological stress, such as drought, nutrition deficiency and plant disease can be distinguished by the monitoring method. Furthermore, we have studied the correlation between the physiological indicators and the degree of stress. Besides realizing the continuous monitoring of crop physiology, the monitoring system and method provide the possibility of machine automatic diagnosis of the plant physiology. Highlights: A newly designed high throughput crop physiology monitoring system and the corresponding monitoring method are described in this study. Different types of stress can induce distinct fluorescence and spectral characteristics, which can be used to evaluate the physiological status of plants.

OTVE turbopump condition monitoring, task E.5

NASA Technical Reports Server (NTRS)

Coleman, Paul T.; Collins, J. J.

1989-01-01

Recent work has been carried out on development of isotope wear analysis and optical and eddy current technologies to provide bearing wear measurements and real time monitoring of shaft speed, shaft axial displacement and shaft orbit of the Orbit Transfer Vehicle hydrostatic bearing tester. Results show shaft axial displacement can be optically measured (at the same time as shaft orbital motion and speed) to within 0.3 mils by two fiberoptic deflectometers. Evaluation of eddy current probes showed that, in addition to measuring shaft orbital motion, they can be used to measure shaft speed without having to machine grooves on the shaft surface as is the usual practice for turbomachinery. The interim results of this condition monitoring effort are presented.

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

PubMed Central

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-01-01

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL. PMID:27258277

Monitoring Temperature and Fan Speed Using Ganglia and Winbond Chips

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCaffrey, Cattie; /SLAC

2006-09-27

Effective monitoring is essential to keep a large group of machines, like the ones at Stanford Linear Accelerator Center (SLAC), up and running. SLAC currently uses Ganglia Monitoring System to observe about 2000 machines, analyzing metrics like CPU usage and I/O rate. However, metrics essential to machine hardware health, such as temperature and fan speed, are not being monitored. Many machines have a Winbond w83782d chip which monitors three temperatures, two of which come from dual CPUs, and returns the information when the sensor command is invoked. Ganglia also provides a feature, gmetric, that allows the users to monitor theirmore » own metrics and incorporate them into the monitoring system. The programming language Perl is chosen to implement a script that invokes the sensors command, extracts the temperature and fan speed information, and calls gmetric with the appropriate arguments. Two machines were used to test the script; the two CPUs on each machine run at about 65 Celsius, which is well within the operating temperature range (The maximum safe temperature range is 77-82 Celsius for the Pentium III processors being used). Installing the script on all machines with a Winbond w83782d chip allows the SLAC Scientific Computing and Computing Services group (SCCS) to better evaluate current cooling methods.« less

Self-assembled software and method of overriding software execution

DOEpatents

Bouchard, Ann M.; Osbourn, Gordon C.

2013-01-08

A computer-implemented software self-assembled system and method for providing an external override and monitoring capability to dynamically self-assembling software containing machines that self-assemble execution sequences and data structures. The method provides an external override machine that can be introduced into a system of self-assembling machines while the machines are executing such that the functionality of the executing software can be changed or paused without stopping the code execution and modifying the existing code. Additionally, a monitoring machine can be introduced without stopping code execution that can monitor specified code execution functions by designated machines and communicate the status to an output device.

High-Speed Edge Trimming of CFRP and Online Monitoring of Performance of Router Tools Using Acoustic Emission

PubMed Central

Prakash, Rangasamy; Krishnaraj, Vijayan; Zitoune, Redouane; Sheikh-Ahmad, Jamal

2016-01-01

Carbon fiber reinforced polymers (CFRPs) have found wide-ranging applications in numerous industrial fields such as aerospace, automotive, and shipping industries due to their excellent mechanical properties that lead to enhanced functional performance. In this paper, an experimental study on edge trimming of CFRP was done with various cutting conditions and different geometry of tools such as helical-, fluted-, and burr-type tools. The investigation involves the measurement of cutting forces for the different machining conditions and its effect on the surface quality of the trimmed edges. The modern cutting tools (router tools or burr tools) selected for machining CFRPs, have complex geometries in cutting edges and surfaces, and therefore a traditional method of direct tool wear evaluation is not applicable. An acoustic emission (AE) sensing was employed for on-line monitoring of the performance of router tools to determine the relationship between AE signal and length of machining for different kinds of geometry of tools. The investigation showed that the router tool with a flat cutting edge has better performance by generating lower cutting force and better surface finish with no delamination on trimmed edges. The mathematical modeling for the prediction of cutting forces was also done using Artificial Neural Network and Regression Analysis. PMID:28773919

Offline detection of broken rotor bars in AC induction motors

NASA Astrophysics Data System (ADS)

Powers, Craig Stephen

ABSTRACT. OFFLINE DETECTION OF BROKEN ROTOR BARS IN AC INDUCTION MOTORS. The detection of the broken rotor bar defect in medium- and large-sized AC induction machines is currently one of the most difficult tasks for the motor condition and monitoring industry. If a broken rotor bar defect goes undetected, it can cause a catastrophic failure of an expensive machine. If a broken rotor bar defect is falsely determined, it wastes time and money to physically tear down and inspect the machine only to find an incorrect diagnosis. Previous work in 2009 at Baker/SKF-USA in collaboration with the Korea University has developed a prototype instrument that has been highly successful in correctly detecting the broken rotor bar defect in ACIMs where other methods have failed. Dr. Sang Bin and his students at the Korea University have been using this prototype instrument to help the industry save money in the successful detection of the BRB defect. A review of the current state of motor conditioning and monitoring technology for detecting the broken rotor bar defect in ACIMs shows improved detection of this fault is still relevant. An analysis of previous work in the creation of this prototype instrument leads into the refactoring of the software and hardware into something more deployable, cost effective and commercially viable.

Fault diagnosis of helical gearbox using acoustic signal and wavelets

NASA Astrophysics Data System (ADS)

Pranesh, SK; Abraham, Siju; Sugumaran, V.; Amarnath, M.

2017-05-01

The efficient transmission of power in machines is needed and gears are an appropriate choice. Faults in gears result in loss of energy and money. The monitoring and fault diagnosis are done by analysis of the acoustic and vibrational signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial faults were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study

Advanced Monitoring to Improve Combustion Turbine/Combined Cycle Reliability, Availability & Maintainability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leonard Angello

2005-09-30

Power generators are concerned with the maintenance costs associated with the advanced turbines that they are purchasing. Since these machines do not have fully established Operation and Maintenance (O&M) track records, power generators face financial risk due to uncertain future maintenance costs. This risk is of particular concern, as the electricity industry transitions to a competitive business environment in which unexpected O&M costs cannot be passed through to consumers. These concerns have accelerated the need for intelligent software-based diagnostic systems that can monitor the health of a combustion turbine in real time and provide valuable information on the machine's performancemore » to its owner/operators. EPRI, Impact Technologies, Boyce Engineering, and Progress Energy have teamed to develop a suite of intelligent software tools integrated with a diagnostic monitoring platform that, in real time, interpret data to assess the 'total health' of combustion turbines. The 'Combustion Turbine Health Management System' (CTHMS) will consist of a series of 'Dynamic Link Library' (DLL) programs residing on a diagnostic monitoring platform that accepts turbine health data from existing monitoring instrumentation. CTHMS interprets sensor and instrument outputs, correlates them to a machine's condition, provide interpretative analyses, project servicing intervals, and estimate remaining component life. In addition, the CTHMS enables real-time anomaly detection and diagnostics of performance and mechanical faults, enabling power producers to more accurately predict critical component remaining useful life and turbine degradation.« less

Intelligent Performance Analysis with a Natural Language Interface

NASA Astrophysics Data System (ADS)

Juuso, Esko K.

2017-09-01

Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

A real-time posture monitoring method for rail vehicle bodies based on machine vision

NASA Astrophysics Data System (ADS)

Liu, Dongrun; Lu, Zhaijun; Cao, Tianpei; Li, Tian

2017-06-01

Monitoring vehicle operation conditions has become significantly important in modern high-speed railway systems. However, the operational impact of monitoring the roll angle of vehicle bodies has principally been limited to tilting trains, while few studies have focused on monitoring the running posture of vehicle bodies during operation. We propose a real-time posture monitoring method to fulfil real-time monitoring requirements, by taking rail surfaces and centrelines as detection references. In realising the proposed method, we built a mathematical computational model based on space coordinate transformations to calculate attitude angles of vehicles in operation and vertical and lateral vibration displacements of single measuring points. Moreover, comparison and verification of reliability between system and field results were conducted. Results show that monitoring of the roll angles of car bodies obtained through the system exhibit variation trends similar to those converted from the dynamic deflection of bogie secondary air springs. The monitoring results of two identical conditions were basically the same, highlighting repeatability and good monitoring accuracy. Therefore, our monitoring results were reliable in reflecting posture changes in running railway vehicles.

Crack identification for rigid pavements using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Bahaddin Ersoz, Ahmet; Pekcan, Onur; Teke, Turker

2017-09-01

Pavement condition assessment is an essential piece of modern pavement management systems as rehabilitation strategies are planned based upon its outcomes. For proper evaluation of existing pavements, they must be continuously and effectively monitored using practical means. Conventionally, truck-based pavement monitoring systems have been in-use in assessing the remaining life of in-service pavements. Although such systems produce accurate results, their use can be expensive and data processing can be time consuming, which make them infeasible considering the demand for quick pavement evaluation. To overcome such problems, Unmanned Aerial Vehicles (UAVs) can be used as an alternative as they are relatively cheaper and easier-to-use. In this study, we propose a UAV based pavement crack identification system for monitoring rigid pavements’ existing conditions. The system consists of recently introduced image processing algorithms used together with conventional machine learning techniques, both of which are used to perform detection of cracks on rigid pavements’ surface and their classification. Through image processing, the distinct features of labelled crack bodies are first obtained from the UAV based images and then used for training of a Support Vector Machine (SVM) model. The performance of the developed SVM model was assessed with a field study performed along a rigid pavement exposed to low traffic and serious temperature changes. Available cracks were classified using the UAV based system and obtained results indicate it ensures a good alternative solution for pavement monitoring applications.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Effects of retrofit emission controls and work practices on perchloroethylene exposures in small dry-cleaning shops.

PubMed

Ewers, Lynda M; Ruder, Avima M; Petersen, Martin R; Earnest, G Scott; Goldenhar, Linda M

2002-02-01

The effectiveness of commercially available interventions for reducing workers' perchloroethylene exposures in three small dry-cleaning shops was evaluated. Depending upon machine configuration, the intervention consisted of the addition of either a refrigerated condenser or a closed-loop carbon adsorber to the existing dry-cleaning machine. These relatively inexpensive (less than $5000) engineering controls were designed to reduce perchloroethylene emissions when dry-cleaning machine doors were opened for loading or unloading. Effectiveness of the interventions was judged by comparing pre- and postintervention perchloroethylene exposures using three types of measurements in each shop: (1) full-shift, personal breathing zone, air monitoring, (2) next-morning, end-exhaled worker breath concentrations of perchloroethylene, and (3) differences in the end-exhaled breath perchloroethylene concentrations before and after opening the dry-cleaning machine door. In general, measurements supported the hypothesis that machine operators' exposures to perchloroethylene can be reduced. However, work practices, especially maintenance practices, influenced exposures more than was originally anticipated. Only owners of dry-cleaning machines in good repair, with few leaks, should consider retrofitting them, and only after consultation with their machine's manufacturer. If machines are in poor condition, a new machine or alternative technology should be considered. Shop owners and employees should never circumvent safety features on dry-cleaning machines.

Process Monitoring Evaluation and Implementation for the Wood Abrasive Machining Process

PubMed Central

Saloni, Daniel E.; Lemaster, Richard L.; Jackson, Steven D.

2010-01-01

Wood processing industries have continuously developed and improved technologies and processes to transform wood to obtain better final product quality and thus increase profits. Abrasive machining is one of the most important of these processes and therefore merits special attention and study. The objective of this work was to evaluate and demonstrate a process monitoring system for use in the abrasive machining of wood and wood based products. The system developed increases the life of the belt by detecting (using process monitoring sensors) and removing (by cleaning) the abrasive loading during the machining process. This study focused on abrasive belt machining processes and included substantial background work, which provided a solid base for understanding the behavior of the abrasive, and the different ways that the abrasive machining process can be monitored. In addition, the background research showed that abrasive belts can effectively be cleaned by the appropriate cleaning technique. The process monitoring system developed included acoustic emission sensors which tended to be sensitive to belt wear, as well as platen vibration, but not loading, and optical sensors which were sensitive to abrasive loading. PMID:22163477

Real-Time Deflection Monitoring for Milling of a Thin-Walled Workpiece by Using PVDF Thin-Film Sensors with a Cantilevered Beam as a Case Study

PubMed Central

Luo, Ming; Liu, Dongsheng; Luo, Huan

2016-01-01

Thin-walled workpieces, such as aero-engine blisks and casings, are usually made of hard-to-cut materials. The wall thickness is very small and it is easy to deflect during milling process under dynamic cutting forces, leading to inaccurate workpiece dimensions and poor surface integrity. To understand the workpiece deflection behavior in a machining process, a new real-time nonintrusive method for deflection monitoring is presented, and a detailed analysis of workpiece deflection for different machining stages of the whole machining process is discussed. The thin-film polyvinylidene fluoride (PVDF) sensor is attached to the non-machining surface of the workpiece to copy the deflection excited by the dynamic cutting force. The relationship between the input deflection and the output voltage of the monitoring system is calibrated by testing. Monitored workpiece deflection results show that the workpiece experiences obvious vibration during the cutter entering the workpiece stage, and vibration during the machining process can be easily tracked by monitoring the deflection of the workpiece. During the cutter exiting the workpiece stage, the workpiece experiences forced vibration firstly, and free vibration exists until the amplitude reduces to zero after the cutter exits the workpiece. Machining results confirmed the suitability of the deflection monitoring system for machining thin-walled workpieces with the application of PVDF sensors. PMID:27626424

Wireless and Powerless Sensing Node System Developed for Monitoring Motors.

PubMed

Lee, Dasheng

2008-08-27

Reliability and maintainability of tooling systems can be improved through condition monitoring of motors. However, it is difficult to deploy sensor nodes due to the harsh environment of industrial plants. Sensor cables are easily damaged, which renders the monitoring system deployed to assure the machine's reliability itself unreliable. A wireless and powerless sensing node integrated with a MEMS (Micro Electro-Mechanical System) sensor, a signal processor, a communication module, and a self-powered generator was developed in this study for implementation of an easily mounted network sensor for monitoring motors. A specially designed communication module transmits a sequence of electromagnetic (EM) pulses in response to the sensor signals. The EM pulses can penetrate through the machine's metal case and delivers signals from the sensor inside the motor to the external data acquisition center. By using induction power, which is generated by the motor's shaft rotation, the sensor node is self-sustaining; therefore, no power line is required. A monitoring system, equipped with novel sensing nodes, was constructed to test its performance. The test results illustrate that, the novel sensing node developed in this study can effectively enhance the reliability of the motor monitoring system and it is expected to be a valuable technology, which will be available to the plant for implementation in a reliable motor management program.

Wireless and Powerless Sensing Node System Developed for Monitoring Motors

PubMed Central

Lee, Dasheng

2008-01-01

Reliability and maintainability of tooling systems can be improved through condition monitoring of motors. However, it is difficult to deploy sensor nodes due to the harsh environment of industrial plants. Sensor cables are easily damaged, which renders the monitoring system deployed to assure the machine's reliability itself unreliable. A wireless and powerless sensing node integrated with a MEMS (Micro Electro-Mechanical System) sensor, a signal processor, a communication module, and a self-powered generator was developed in this study for implementation of an easily mounted network sensor for monitoring motors. A specially designed communication module transmits a sequence of electromagnetic (EM) pulses in response to the sensor signals. The EM pulses can penetrate through the machine's metal case and delivers signals from the sensor inside the motor to the external data acquisition center. By using induction power, which is generated by the motor's shaft rotation, the sensor node is self-sustaining; therefore, no power line is required. A monitoring system, equipped with novel sensing nodes, was constructed to test its performance. The test results illustrate that, the novel sensing node developed in this study can effectively enhance the reliability of the motor monitoring system and it is expected to be a valuable technology, which will be available to the plant for implementation in a reliable motor management program. PMID:27873798

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

PubMed

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

PubMed Central

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

A High Performance Torque Sensor for Milling Based on a Piezoresistive MEMS Strain Gauge

PubMed Central

Qin, Yafei; Zhao, Yulong; Li, Yingxue; Zhao, You; Wang, Peng

2016-01-01

In high speed and high precision machining applications, it is important to monitor the machining process in order to ensure high product quality. For this purpose, it is essential to develop a dynamometer with high sensitivity and high natural frequency which is suited to these conditions. This paper describes the design, calibration and performance of a milling torque sensor based on piezoresistive MEMS strain. A detailed design study is carried out to optimize the two mutually-contradictory indicators sensitivity and natural frequency. The developed torque sensor principally consists of a thin-walled cylinder, and a piezoresistive MEMS strain gauge bonded on the surface of the sensing element where the shear strain is maximum. The strain gauge includes eight piezoresistances and four are connected in a full Wheatstone circuit bridge, which is used to measure the applied torque force during machining procedures. Experimental static calibration results show that the sensitivity of torque sensor has been improved to 0.13 mv/Nm. A modal impact test indicates that the natural frequency of torque sensor reaches 1216 Hz, which is suitable for high speed machining processes. The dynamic test results indicate that the developed torque sensor is stable and practical for monitoring the milling process. PMID:27070620

Statistical quality control for volumetric modulated arc therapy (VMAT) delivery by using the machine's log data

NASA Astrophysics Data System (ADS)

Cheong, Kwang-Ho; Lee, Me-Yeon; Kang, Sei-Kwon; Yoon, Jai-Woong; Park, Soah; Hwang, Taejin; Kim, Haeyoung; Kim, Kyoung Ju; Han, Tae Jin; Bae, Hoonsik

2015-07-01

The aim of this study is to set up statistical quality control for monitoring the volumetric modulated arc therapy (VMAT) delivery error by using the machine's log data. Eclipse and a Clinac iX linac with the RapidArc system (Varian Medical Systems, Palo Alto, USA) are used for delivery of the VMAT plan. During the delivery of the RapidArc fields, the machine determines the delivered monitor units (MUs) and the gantry angle's position accuracy and the standard deviations of the MU ( σMU: dosimetric error) and the gantry angle ( σGA: geometric error) are displayed on the console monitor after completion of the RapidArc delivery. In the present study, first, the log data were analyzed to confirm its validity and usability; then, statistical process control (SPC) was applied to monitor the σMU and the σGA in a timely manner for all RapidArc fields: a total of 195 arc fields for 99 patients. The MU and the GA were determined twice for all fields, that is, first during the patient-specific plan QA and then again during the first treatment. The sMU and the σGA time series were quite stable irrespective of the treatment site; however, the sGA strongly depended on the gantry's rotation speed. The σGA of the RapidArc delivery for stereotactic body radiation therapy (SBRT) was smaller than that for the typical VMAT. Therefore, SPC was applied for SBRT cases and general cases respectively. Moreover, the accuracy of the potential meter of the gantry rotation is important because the σGA can change dramatically due to its condition. By applying SPC to the σMU and σGA, we could monitor the delivery error efficiently. However, the upper and the lower limits of SPC need to be determined carefully with full knowledge of the machine and log data.

Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

PubMed Central

Yu, Yang; Niederleithinger, Ernst; Li, Jianchun; Wiggenhauser, Herbert

2017-01-01

This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%. PMID:29258274

Introducing passive acoustic filter in acoustic based condition monitoring: Motor bike piston-bore fault identification

NASA Astrophysics Data System (ADS)

Jena, D. P.; Panigrahi, S. N.

2016-03-01

Requirement of designing a sophisticated digital band-pass filter in acoustic based condition monitoring has been eliminated by introducing a passive acoustic filter in the present work. So far, no one has attempted to explore the possibility of implementing passive acoustic filters in acoustic based condition monitoring as a pre-conditioner. In order to enhance the acoustic based condition monitoring, a passive acoustic band-pass filter has been designed and deployed. Towards achieving an efficient band-pass acoustic filter, a generalized design methodology has been proposed to design and optimize the desired acoustic filter using multiple filter components in series. An appropriate objective function has been identified for genetic algorithm (GA) based optimization technique with multiple design constraints. In addition, the sturdiness of the proposed method has been demonstrated in designing a band-pass filter by using an n-branch Quincke tube, a high pass filter and multiple Helmholtz resonators. The performance of the designed acoustic band-pass filter has been shown by investigating the piston-bore defect of a motor-bike using engine noise signature. On the introducing a passive acoustic filter in acoustic based condition monitoring reveals the enhancement in machine learning based fault identification practice significantly. This is also a first attempt of its own kind.

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

PubMed

Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

2016-07-01

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Novel tool wear monitoring method in milling difficult-to-machine materials using cutting chip formation

NASA Astrophysics Data System (ADS)

Zhang, P. P.; Guo, Y.; Wang, B.

2017-05-01

The main problems in milling difficult-to-machine materials are the high cutting temperature and rapid tool wear. However it is impossible to investigate tool wear in machining. Tool wear and cutting chip formation are two of the most important representations for machining efficiency and quality. The purpose of this paper is to develop the model of tool wear with cutting chip formation (width of chip and radian of chip) on difficult-to-machine materials. Thereby tool wear is monitored by cutting chip formation. A milling experiment on the machining centre with three sets cutting parameters was performed to obtain chip formation and tool wear. The experimental results show that tool wear increases gradually along with cutting process. In contrast, width of chip and radian of chip decrease. The model is developed by fitting the experimental data and formula transformations. The most of monitored errors of tool wear by the chip formation are less than 10%. The smallest error is 0.2%. Overall errors by the radian of chip are less than the ones by the width of chip. It is new way to monitor and detect tool wear by cutting chip formation in milling difficult-to-machine materials.

Using sound to unmask losses disguised as wins in multiline slot machines.

PubMed

Dixon, Mike J; Collins, Karen; Harrigan, Kevin A; Graydon, Candice; Fugelsang, Jonathan A

2015-03-01

Losses disguised as wins (LDWs) are slot machine outcomes where participants bet on multiple lines and win back less than their wager. Despite losing money, the machine celebrates these outcomes with reinforcing sights and sounds. Here, we sought to show that psychophysically and psychologically, participants treat LDWs as wins, but that we could expose LDWs as losses by using negative sounds as feedback. 157 participants were allocated into one of three conditions: a standard sound condition where LDWs, despite being losses, are paired with winning sights and sounds; a silent condition, where LDWs are paired with silence; and a negative sound condition where LDWs and regular losses are both followed by a negative sound. After viewing a paytable, participants conducted 300 spins on a slot machine simulator while heart rate deceleration (HRD) and skin conductance responses (SCRs) were monitored. Participants were then shown 20 different spin outcomes including LDWs and asked whether they had won or lost on that outcome. Participants then estimated on how many spins (out of 300) they won more than they wagered. SCRs were similar for losses and LDWs (both smaller than actual wins). HRD, however, was steeper for both wins and LDWs, compared to losses. In the standard condition, a majority of participants (mis)categorized LDWs as wins, and significantly overestimated the number of times they actually won. In the negative sound condition, this pattern was reversed; most participants correctly categorized LDWs as losses, and they gave high-fidelity win estimates. We conclude that participants both think and physiologically react to LDWs as though they are wins, a miscategorization that misleads them to think that they are winning more often than they actually are. Sound can be used to effectively prevent this misconception and unmask the disguise of LDWs.

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

Detection of periods of food intake using Support Vector Machines.

PubMed

Lopez-Meyer, Paulo; Schuckers, Stephanie; Makeyev, Oleksandr; Sazonov, Edward

2010-01-01

Studies of obesity and eating disorders need objective tools of Monitoring of Ingestive Behavior (MIB) that can detect and characterize food intake. In this paper we describe detection of food intake by a Support Vector Machine classifier trained on time history of chews and swallows. The training was performed on data collected from 18 subjects in 72 experiments involving eating and other activities (for example, talking). The highest accuracy of detecting food intake (94%) was achieved in configuration where both chews and swallows were used as predictors. Using only swallowing as a predictor resulted in 80% accuracy. Experimental results suggest that these two predictors may be used for differentiation between periods of resting and food intake with a resolution of 30 seconds. Proposed methods may be utilized for development of an accurate, inexpensive, and non-intrusive methodology to objectively monitor food intake in free living conditions.

Information Fusion of Conflicting Input Data.

PubMed

Mönks, Uwe; Dörksen, Helene; Lohweg, Volker; Hübner, Michael

2016-10-29

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μ BalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

Information Fusion of Conflicting Input Data

PubMed Central

Mönks, Uwe; Dörksen, Helene; Lohweg, Volker; Hübner, Michael

2016-01-01

Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible. PMID:27801874

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

NASA Astrophysics Data System (ADS)

Tobon-Mejia, D. A.; Medjaher, K.; Zerhouni, N.

2012-04-01

The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a computer numerical control (CNC) tool machine and the estimation of its remaining useful life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.

Detecting weak position fluctuations from encoder signal using singular spectrum analysis.

PubMed

Xu, Xiaoqiang; Zhao, Ming; Lin, Jing

2017-11-01

Mechanical fault or defect will cause some weak fluctuations to the position signal. Detection of such fluctuations via encoders can help determine the health condition and performance of the machine, and offer a promising alternative to the vibration-based monitoring scheme. However, besides the interested fluctuations, encoder signal also contains a large trend and some measurement noise. In applications, the trend is normally several orders larger than the concerned fluctuations in magnitude, which makes it difficult to detect the weak fluctuations without signal distortion. In addition, the fluctuations can be complicated and amplitude modulated under non-stationary working condition. To overcome this issue, singular spectrum analysis (SSA) is proposed for detecting weak position fluctuations from encoder signal in this paper. It enables complicated encode signal to be reduced into several interpretable components including a trend, a set of periodic fluctuations and noise. A numerical simulation is given to demonstrate the performance of the method, it shows that SSA outperforms empirical mode decomposition (EMD) in terms of capability and accuracy. Moreover, linear encoder signals from a CNC machine tool are analyzed to determine the magnitudes and sources of fluctuations during feed motion. The proposed method is proven to be feasible and reliable for machinery condition monitoring. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

An Intelligent Sensor Array Distributed System for Vibration Analysis and Acoustic Noise Characterization of a Linear Switched Reluctance Actuator

PubMed Central

Salvado, José; Espírito-Santo, António; Calado, Maria

2012-01-01

This paper proposes a distributed system for analysis and monitoring (DSAM) of vibrations and acoustic noise, which consists of an array of intelligent modules, sensor modules, communication bus and a host PC acting as data center. The main advantages of the DSAM are its modularity, scalability, and flexibility for use of different type of sensors/transducers, with analog or digital outputs, and for signals of different nature. Its final cost is also significantly lower than other available commercial solutions. The system is reconfigurable, can operate either with synchronous or asynchronous modes, with programmable sampling frequencies, 8-bit or 12-bit resolution and a memory buffer of 15 kbyte. It allows real-time data-acquisition for signals of different nature, in applications that require a large number of sensors, thus it is suited for monitoring of vibrations in Linear Switched Reluctance Actuators (LSRAs). The acquired data allows the full characterization of the LSRA in terms of its response to vibrations of structural origins, and the vibrations and acoustic noise emitted under normal operation. The DSAM can also be used for electrical machine condition monitoring, machine fault diagnosis, structural characterization and monitoring, among other applications. PMID:22969364

Heart Rate Monitors

NASA Technical Reports Server (NTRS)

1990-01-01

Under a NASA grant, Dr. Robert M. Davis and Dr. William M. Portnoy came up with a new type of electrocardiographic electrode that would enable long term use on astronauts. Their invention was an insulated capacitive electrode constructed of a thin dielectric film. NASA subsequently licensed the electrode technology to Richard Charnitski, inventor of the VersaClimber, who founded Heart Rate, Inc., to further develop and manufacture personal heart monitors and to produce exercise machines using the technology for the physical fitness, medical and home markets. Same technology is on both the Home and Institutional Model VersaClimbers. On the Home Model an infrared heart beat transmitter is worn under exercise clothing. Transmitted heart rate is used to control the work intensity on the VersaClimber using the heart rate as the speedometer of the exercise. This offers advantages to a full range of users from the cardiac rehab patient to the high level physical conditioning of elite athletes. The company manufactures and markets five models of the 1*2*3 HEART RATE monitors that are used wherever people exercise to accurately monitor their heart rate. Company is developing a talking heart rate monitor that works with portable headset radios. A version of the heart beat transmitter will be available to the manufacturers of other aerobic exercise machines.

Tapping and listening: a new approach to bolt looseness monitoring

NASA Astrophysics Data System (ADS)

Kong, Qingzhao; Zhu, Junxiao; Ho, Siu Chun Michael; Song, Gangbing

2018-07-01

Bolted joints are among the most common building blocks used across different types of structures, and are often the key components that sew all other structural parts together. Monitoring and assessment of looseness in bolted structures is one of the most attractive topics in mechanical, aerospace, and civil engineering. This paper presents a new percussion-based non-destructive approach to determine the health condition of bolted joints with the help of machine learning. The proposed method is very similar to the percussive diagnostic techniques used in clinical examinations to diagnose the health of patients. Due to the different interfacial properties among the bolts, nuts and the host structure, bolted joints can generate unique sounds when it is excited by impacts, such as from tapping. Power spectrum density, as a signal feature, was used to recognize and classify recorded tapping data. A machine learning model using the decision tree method was employed to identify the bolt looseness level. Experiments demonstrated that the newly proposed method for bolt looseness detection is very easy to implement by ‘listening to tapping’ and the monitoring accuracy is very high. With the rapid in robotics, the proposed approach has great potential to be implemented with intimately weaving robotics and machine learning to produce a cyber-physical system that can automatically inspect and determine the health of a structure.

Experimental Validation of a Resilient Monitoring and Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wen-Chiao Lin; Kris R. E. Villez; Humberto E. Garcia

2014-05-01

Complex, high performance, engineering systems have to be closely monitored and controlled to ensure safe operation and protect public from potential hazards. One of the main challenges in designing monitoring and control algorithms for these systems is that sensors and actuators may be malfunctioning due to malicious or natural causes. To address this challenge, this paper addresses a resilient monitoring and control (ReMAC) system by expanding previously developed resilient condition assessment monitoring systems and Kalman filter-based diagnostic methods and integrating them with a supervisory controller developed here. While the monitoring and diagnostic algorithms assess plant cyber and physical health conditions,more » the supervisory controller selects, from a set of candidates, the best controller based on the current plant health assessments. To experimentally demonstrate its enhanced performance, the developed ReMAC system is then used for monitoring and control of a chemical reactor with a water cooling system in a hardware-in-the-loop setting, where the reactor is computer simulated and the water cooling system is implemented by a machine condition monitoring testbed at Idaho National Laboratory. Results show that the ReMAC system is able to make correct plant health assessments despite sensor malfunctioning due to cyber attacks and make decisions that achieve best control actions despite possible actuator malfunctioning. Monitoring challenges caused by mismatches between assumed system component models and actual measurements are also identified for future work.« less

Monitoring Method of Cutting Force by Using Additional Spindle Sensors

NASA Astrophysics Data System (ADS)

Sarhan, Ahmed Aly Diaa; Matsubara, Atsushi; Sugihara, Motoyuki; Saraie, Hidenori; Ibaraki, Soichi; Kakino, Yoshiaki

This paper describes a monitoring method of cutting forces for end milling process by using displacement sensors. Four eddy-current displacement sensors are installed on the spindle housing of a machining center so that they can detect the radial motion of the rotating spindle. Thermocouples are also attached to the spindle structure in order to examine the thermal effect in the displacement sensing. The change in the spindle stiffness due to the spindle temperature and the speed is investigated as well. Finally, the estimation performance of cutting forces using the spindle displacement sensors is experimentally investigated by machining tests on carbon steel in end milling operations under different cutting conditions. It is found that the monitoring errors are attributable to the thermal displacement of the spindle, the time lag of the sensing system, and the modeling error of the spindle stiffness. It is also shown that the root mean square errors between estimated and measured amplitudes of cutting forces are reduced to be less than 20N with proper selection of the linear stiffness.

System for monitoring an industrial or biological process

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.; Vilim, Rick B.; White, Andrew M.

1998-01-01

A method and apparatus for monitoring and responding to conditions of an industrial process. Industrial process signals, such as repetitive manufacturing, testing and operational machine signals, are generated by a system. Sensor signals characteristic of the process are generated over a time length and compared to reference signals over the time length. The industrial signals are adjusted over the time length relative to the reference signals, the phase shift of the industrial signals is optimized to the reference signals and the resulting signals output for analysis by systems such as SPRT.

System for monitoring an industrial or biological process

DOEpatents

Gross, K.C.; Wegerich, S.W.; Vilim, R.B.; White, A.M.

1998-06-30

A method and apparatus are disclosed for monitoring and responding to conditions of an industrial process. Industrial process signals, such as repetitive manufacturing, testing and operational machine signals, are generated by a system. Sensor signals characteristic of the process are generated over a time length and compared to reference signals over the time length. The industrial signals are adjusted over the time length relative to the reference signals, the phase shift of the industrial signals is optimized to the reference signals and the resulting signals output for analysis by systems such as SPRT. 49 figs.

A microcomputer network for control of a continuous mining machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schiffbauer, W.H.

1993-12-31

This report details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines and installed on a continuous mining machine. The network consists of microcomputers that are connected together via a single twisted-pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers, in conjunction with the appropriate sensors, provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and control the continuous miningmore » machine. Because of the network`s generic structure, it can be installed on most mining machines.« less

PCD tool wear and its monitoring in machining tungsten

NASA Astrophysics Data System (ADS)

Wang, Lijiang; Zhang, Zhenlie; Sun, Qi; Liu, Pin

The views of Chinese and foreign researchers are quite different as to whether or not polycrystalline diamond (PCD) tools can machine tungsten that is used in the aerospace and electronic industries. A study is presented that shows the possibility of machining tungsten, and a new method is developed for monitoring the tool wear in production.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs

NASA Astrophysics Data System (ADS)

Simon, Victor; Johansson, Carl-Anders; Galar, Diego

2017-09-01

All electric powered machines offer the possibility of extracting information and calculating Key Performance Indicators (KPIs) from the electric current signal. Depending on the time window, sampling frequency and type of analysis, different indicators from the micro to macro level can be calculated for such aspects as maintenance, production, energy consumption etc. On the micro-level, the indicators are generally used for condition monitoring and diagnostics and are normally based on a short time window and a high sampling frequency. The macro indicators are normally based on a longer time window with a slower sampling frequency and are used as indicators for overall performance, cost or consumption. The indicators can be calculated directly from the current signal but can also be based on a combination of information from the current signal and operational data like rpm, position etc. One or several of those indicators can be used for prediction and prognostics of a machine's future behavior. This paper uses this technique to calculate indicators for maintenance and energy optimization in electric powered machines and fleets of machines, especially machine tools.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Alfi, S.; Bruni, S.

2016-06-01

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

A machine for the preliminary investigation of design features influencing the wear behaviour of knee prostheses.

PubMed

McGloughlin, T M; Murphy, D M; Kavanagh, A G

2004-01-01

Degradation of tibial inserts in vivo has been found to be multifactorial in nature, resulting in a complex interaction of many variables. A range of kinematic conditions occurs at the tibio-femoral interface, giving rise to various degrees of rolling and sliding at this interface. The movement of the tibio-femoral contact point may be an influential factor in the overall wear of ultra-high molecular weight polyethylene (UHMWPE) tibial components. As part of this study a three-station wear-test machine was designed and built to investigate the influence of rolling and sliding on the wear behaviour of specific design aspects of contemporary knee prostheses. Using the machine, it is possible to monitor the effect of various slide roll ratios on the performance of contemporary bearing designs from a geometrical and materials perspective.

Shaft instantaneous angular speed for blade vibration in rotating machine

NASA Astrophysics Data System (ADS)

Gubran, Ahmed A.; Sinha, Jyoti K.

2014-02-01

Reliable blade health monitoring (BHM) in rotating machines like steam turbines and gas turbines, is a topic of research since decades to reduce machine down time, maintenance costs and to maintain the overall safety. Transverse blade vibration is often transmitted to the shaft as torsional vibration. The shaft instantaneous angular speed (IAS) is nothing but the representing the shaft torsional vibration. Hence the shaft IAS has been extracted from the measured encoder data during machine run-up to understand the blade vibration and to explore the possibility of reliable assessment of blade health. A number of experiments on an experimental rig with a bladed disk were conducted with healthy but mistuned blades and with different faults simulation in the blades. The measured shaft torsional vibration shows a distinct difference between the healthy and the faulty blade conditions. Hence, the observations are useful for the BHM in future. The paper presents the experimental setup, simulation of blade faults, experiments conducted, observations and results.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

NASA Astrophysics Data System (ADS)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Condition monitoring of a prototype turbine. Description of the system and main results

NASA Astrophysics Data System (ADS)

Valero, C.; Egusquiza, E.; Presas, A.; Valentin, D.; Egusquiza, M.; Bossio, M.

2017-04-01

The fast change in new renewable energy is affecting directly the required operating range of hydropower plants. According to the present demand of electricity, it is necessary to generate different levels of power. Because of its ease to regulate and its huge storage capacity of energy, hydropower is the unique energy source that can adapt to the demand. Today, the required operating range of turbine units is expected to extend from part load to overload. These extreme operations points can cause several pressure pulsations, cavitation and vibrations in different parts of the machine. To determine the effects on the machine, vibration measurements are necessary in actual machines. Vibrations can be used for machinery protection and to identify problems in the machine (diagnosis). In this paper, some results obtained in a hydropower plant are presented. The variation of global levels and vibratory signatures has been analysed as function as gross head, transducer location and operating points.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

A control technology evaluation of state-of-the-art, perchloroethylene dry-cleaning machines.

PubMed

Earnest, G Scott

2002-05-01

NIOSH researchers evaluated the ability of fifth-generation dry-cleaning machines to control occupational exposure to perchloroethylene (PERC). Use of these machines is mandated in some countries; however, less than 1 percent of all U.S. shops have them. A study was conducted at a U.S. dry-cleaning shop where two fifth-generation machines were used. Both machines had a refrigerated condenser as a primary control and a carbon adsorber as a secondary control to recover PERC vapors during the dry cycle. These machines were designed to lower the PERC concentration in the cylinder at the end of the dry cycle to below 290 ppm. A single-beam infrared photometer continuously monitors the PERC concentration in the machine cylinder, and a door interlock prevents opening until the concentration is below 290 ppm. Personal breathing zone air samples were measured for the machine operator and presser. The operator had time-weighted average (TWA) PERC exposures that were less than 2 ppm. Highest exposures occurred during loading and unloading the machine and when performing routine machine maintenance. All presser samples were below the limit of detection. Real-time video exposure monitoring showed that the operator had peak exposures near 160 ppm during loading and unloading the machine (below the OSHA maximum of 300 ppm). This exposure (160 ppm) is an order of magnitude lower than exposures with more traditional machines that are widely used in the United States. The evaluated machines were very effective at reducing TWA PERC exposures as well as peak exposures that occur during machine loading and unloading. State-of-the-art dry-cleaning machines equipped with refrigerated condensers, carbon adsorbers, drum monitors, and door interlocks can provide substantially better protection than more traditional machines that are widely used in the United States.

Microcomputer network for control of a continuous mining machine. Information circular/1993

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schiffbauer, W.H.

1993-01-01

The paper details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines, and installed on a Joy 14 continuous mining machine. The network consists of microcomputers that are connected together via a single twisted pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers in conjunction with the appropriate sensors provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and controlmore » the continuous mining machine. Although the network was installed on a Joy 14 continuous mining machine, its use extends beyond it. Its generic structure lends itself to installation onto most mining machine types.« less

Using GPS to evaluate productivity and performance of forest machine systems

Treesearch

Steven E. Taylor; Timothy P. McDonald; Matthew W. Veal; Ton E. Grift

2001-01-01

This paper reviews recent research and operational applications of using GPS as a tool to help monitor the locations, travel patterns, performance, and productivity of forest machines. The accuracy of dynamic GPS data collected on forest machines under different levels of forest canopy is reviewed first. Then, the paper focuses on the use of GPS for monitoring forest...

Design of a Sensor System for On-Line Monitoring of Contact Pressure in Chalcographic Printing.

PubMed

Jiménez, José Antonio; Meca, Francisco Javier; Santiso, Enrique; Martín, Pedro

2017-09-05

Chalcographic printer is the name given to a specific type of press which is used to transfer the printing of a metal-based engraved plate onto paper. The printing system consists of two rollers for pressing and carrying a metal plate onto which an engraved inked plate is placed. When the driving mechanism is operated, the pressure exerted by the rollers, also called contact pressure, allows the engraved image to be transferred into paper, thereby obtaining the final image. With the aim of ensuring the quality of the result, in terms of good and even transfer of ink, the contact pressure must be uniform. Nowadays, the strategies utilized to measure the pressure are implemented off-line, i.e., when the press machines are shut down for maintenance, which poses limitations. This paper proposes a novel sensor system aimed at monitoring the pressure exerted by the rollers on the engraved plate while chalcographic printer is operating, i.e., on-line. The purpose is two-fold: firstly, real-time monitoring reduces the number of breakdown repairs required, reduces machine downtime and reduces the number of low-quality engravings, which increases productivity and revenues; and secondly, the on-line monitoring and register of the process parameters allows the printing process to be reproducible even with changes in the environmental conditions or other factors such as the wear of the parts that constitute the mechanical system and a change in the dimensions of the printing materials. The proposed system consists of a strain gauge-based load cell and conditioning electronics to sense and treat the signals.

Design of a Sensor System for On-Line Monitoring of Contact Pressure in Chalcographic Printing

PubMed Central

Jiménez, José Antonio; Meca, Francisco Javier; Santiso, Enrique; Martín, Pedro

2017-01-01

Chalcographic printer is the name given to a specific type of press which is used to transfer the printing of a metal-based engraved plate onto paper. The printing system consists of two rollers for pressing and carrying a metal plate onto which an engraved inked plate is placed. When the driving mechanism is operated, the pressure exerted by the rollers, also called contact pressure, allows the engraved image to be transferred into paper, thereby obtaining the final image. With the aim of ensuring the quality of the result, in terms of good and even transfer of ink, the contact pressure must be uniform. Nowadays, the strategies utilized to measure the pressure are implemented off-line, i.e., when the press machines are shut down for maintenance, which poses limitations. This paper proposes a novel sensor system aimed at monitoring the pressure exerted by the rollers on the engraved plate while chalcographic printer is operating, i.e., on-line. The purpose is two-fold: firstly, real-time monitoring reduces the number of breakdown repairs required, reduces machine downtime and reduces the number of low-quality engravings, which increases productivity and revenues; and secondly, the on-line monitoring and register of the process parameters allows the printing process to be reproducible even with changes in the environmental conditions or other factors such as the wear of the parts that constitute the mechanical system and a change in the dimensions of the printing materials. The proposed system consists of a strain gauge-based load cell and conditioning electronics to sense and treat the signals. PMID:28872583

Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information

DOEpatents

Hively, Lee M.

2014-09-16

Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

From pilot's associate to satellite controller's associate

NASA Technical Reports Server (NTRS)

Neyland, David L.; Lizza, Carl; Merkel, Philip A.

1992-01-01

Associate technology is an emerging engineering discipline wherein intelligent automation can significantly augment the performance of man-machine systems. An associate system is one that monitors operator activity and adapts its operational behavior accordingly. Associate technology is most effectively applied when mapped into management of the human-machine interface and display-control loop in typical manned systems. This paper addresses the potential for application of associate technology into the arena of intelligent command and control of satellite systems, from diagnosis of onboard and onground of satellite systems fault conditions, to execution of nominal satellite control functions. Rather than specifying a specific solution, this paper draws parallels between the Pilot's Associate concept and the domain of satellite control.

Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

NASA Technical Reports Server (NTRS)

Gupta, U. K.; Ali, M.

1988-01-01

The theoretical basis and operation of LEBEX, a machine-learning system for jet-engine performance monitoring, are described. The behavior of the engine is modeled in terms of four parameters (the rotational speeds of the high- and low-speed sections and the exhaust and combustion temperatures), and parameter variations indicating malfunction are transformed into structural representations involving instances and events. LEBEX extracts descriptors from a set of training data on normal and faulty engines, represents them hierarchically in a knowledge base, and uses them to diagnose and predict faults on a real-time basis. Diagrams of the system architecture and printouts of typical results are shown.

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

Development of hardware system using temperature and vibration maintenance models integration concepts for conventional machines monitoring: a case study

NASA Astrophysics Data System (ADS)

Adeyeri, Michael Kanisuru; Mpofu, Khumbulani; Kareem, Buliaminu

2016-03-01

This article describes the integration of temperature and vibration models for maintenance monitoring of conventional machinery parts in which their optimal and best functionalities are affected by abnormal changes in temperature and vibration values thereby resulting in machine failures, machines breakdown, poor quality of products, inability to meeting customers' demand, poor inventory control and just to mention a few. The work entails the use of temperature and vibration sensors as monitoring probes programmed in microcontroller using C language. The developed hardware consists of vibration sensor of ADXL345, temperature sensor of AD594/595 of type K thermocouple, microcontroller, graphic liquid crystal display, real time clock, etc. The hardware is divided into two: one is based at the workstation (majorly meant to monitor machines behaviour) and the other at the base station (meant to receive transmission of machines information sent from the workstation), working cooperatively for effective functionalities. The resulting hardware built was calibrated, tested using model verification and validated through principles pivoted on least square and regression analysis approach using data read from the gear boxes of extruding and cutting machines used for polyethylene bag production. The results got therein confirmed related correlation existing between time, vibration and temperature, which are reflections of effective formulation of the developed concept.

Comparison of 5 reflectance meters for capillary blood glucose determination.

PubMed

Kolopp, M; Louis, J; Pointel, J P; Kohler, F; Drouin, P; Debry, G

1983-03-01

Manufacturing quality, accuracy and users opinion (i.e. medical and nurses staff and patients) were compared among five Destrostix reading reflectance-meters for home-blood-glucose-monitoring. Two machines (dextrometer and glucometer) equipped with microprocessors, integrated circuits and good quality wiring are best made. Reflectance-meter capillary blood glucose measurements were found to be accurate enough for home-blood-glucose-monitoring, compared to a reference method. However, two machines from the same brand were different in blood glucose accuracy. Glucocheck had poorest results. Users prefer small sized, battery powered machines. Glucometer appears to be best suited to home-blood-glucose-monitoring.

Design and validation of wireless system for oil monitoring base on optical sensing unit

NASA Astrophysics Data System (ADS)

Niu, Liqun; Wang, Weiming; Zhang, Shuaishuai; Li, Zhirui; Yu, Yan; Huang, Hui

2017-04-01

According to the situation of oil leakage and the development of oil detection technology, a wireless monitoring system, combining with the sensor technology, optical measurement technology, and wireless technology, is designed. In this paper, the architecture of a wireless system is designed. In the hardware, the collected data, acquired by photoelectric conversion and analog to digital conversion equipment, will be sent to the upper machine where they are saved and analyzed. The experimental results reveals that the wireless system has the characteristics of higher precision, more real-time and more convenient installation, it can reflect the condition of the measuring object truly and implement the dynamic monitoring for a long time on-site, stability—thus it has a good application prospect in the oil monitoring filed.

Statistical analysis on the signals monitoring multiphase flow patterns in pipeline-riser system

NASA Astrophysics Data System (ADS)

Ye, Jing; Guo, Liejin

2013-07-01

The signals monitoring petroleum transmission pipeline in offshore oil industry usually contain abundant information about the multiphase flow on flow assurance which includes the avoidance of most undesirable flow pattern. Therefore, extracting reliable features form these signals to analyze is an alternative way to examine the potential risks to oil platform. This paper is focused on characterizing multiphase flow patterns in pipeline-riser system that is often appeared in offshore oil industry and finding an objective criterion to describe the transition of flow patterns. Statistical analysis on pressure signal at the riser top is proposed, instead of normal prediction method based on inlet and outlet flow conditions which could not be easily determined during most situations. Besides, machine learning method (least square supported vector machine) is also performed to classify automatically the different flow patterns. The experiment results from a small-scale loop show that the proposed method is effective for analyzing the multiphase flow pattern.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Automation and robotics technology for intelligent mining systems

NASA Technical Reports Server (NTRS)

Welsh, Jeffrey H.

1989-01-01

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

Structural health monitoring of wind turbine blades

NASA Astrophysics Data System (ADS)

Rumsey, Mark A.; Paquette, Joshua A.

2008-03-01

As electric utility wind turbines increase in size, and correspondingly, increase in initial capital investment cost, there is an increasing need to monitor the health of the structure. Acquiring an early indication of structural or mechanical problems allows operators to better plan for maintenance, possibly operate the machine in a de-rated condition rather than taking the unit off-line, or in the case of an emergency, shut the machine down to avoid further damage. This paper describes several promising structural health monitoring (SHM) techniques that were recently exercised during a fatigue test of a 9 meter glass-epoxy and carbon-epoxy wind turbine blade. The SHM systems were implemented by teams from NASA Kennedy Space Center, Purdue University and Virginia Tech. A commercial off-the-shelf acoustic emission (AE) NDT system gathered blade AE data throughout the test. At a fatigue load cycle rate around 1.2 Hertz, and after more than 4,000,000 fatigue cycles, the blade was diagnostically and visibly failing at the out-board blade spar-cap termination point at 4.5 meters. For safety reasons, the test was stopped just before the blade completely failed. This paper provides an overview of the SHM and NDT system setups and some current test results.

A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing.

PubMed

Oresko, Joseph J; Duschl, Heather; Cheng, Allen C

2010-05-01

Cardiovascular disease (CVD) is the single leading cause of global mortality and is projected to remain so. Cardiac arrhythmia is a very common type of CVD and may indicate an increased risk of stroke or sudden cardiac death. The ECG is the most widely adopted clinical tool to diagnose and assess the risk of arrhythmia. ECGs measure and display the electrical activity of the heart from the body surface. During patients' hospital visits, however, arrhythmias may not be detected on standard resting ECG machines, since the condition may not be present at that moment in time. While Holter-based portable monitoring solutions offer 24-48 h ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline. In this paper, we seek to unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis solution using smartphones. Specifically, we developed two smartphone-based wearable CVD-detection platforms capable of performing real-time ECG acquisition and display, feature extraction, and beat classification. Furthermore, the same statistical summaries available on resting ECG machines are provided.

Monitoring machining conditions by infrared images

NASA Astrophysics Data System (ADS)

Borelli, Joao E.; Gonzaga Trabasso, Luis; Gonzaga, Adilson; Coelho, Reginaldo T.

2001-03-01

During machining process the knowledge of the temperature is the most important factor in tool analysis. It allows to control main factors that influence tool use, life time and waste. The temperature in the contact area between the piece and the tool is resulting from the material removal in cutting operation and it is too difficult to be obtained because the tool and the work piece are in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements in the process: tool, work piece and chips. Each gray tone in the image corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is gotten by the previous of infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed of three modules. The first module makes the image acquisition and processing. The second module makes the feature image extraction and performs the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and supplies the tool state diagnostic as output.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

NASA Astrophysics Data System (ADS)

Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

2012-01-01

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

A method to identify the main mode of machine tool under operating conditions

NASA Astrophysics Data System (ADS)

Wang, Daming; Pan, Yabing

2017-04-01

The identification of the modal parameters under experimental conditions is the most common procedure when solving the problem of machine tool structure vibration. However, the influence of each mode on the machine tool vibration in real working conditions remains unknown. In fact, the contributions each mode makes to the machine tool vibration during machining process are different. In this article, an active excitation modal analysis is applied to identify the modal parameters in operational condition, and the Operating Deflection Shapes (ODS) in frequencies of high level vibration that affect the quality of machining in real working conditions are obtained. Then, the ODS is decomposed by the mode shapes which are identified in operational conditions. So, the contributions each mode makes to machine tool vibration during machining process are got by decomposition coefficients. From the previous steps, we can find out the main modes which effect the machine tool more significantly in working conditions. This method was also verified to be effective by experiments.

Research of a smart cutting tool based on MEMS strain gauge

NASA Astrophysics Data System (ADS)

Zhao, Y.; Zhao, Y. L.; Shao, YW; Hu, T. J.; Zhang, Q.; Ge, X. H.

2018-03-01

Cutting force is an important factor that affects machining accuracy, cutting vibration and tool wear. Machining condition monitoring by cutting force measurement is a key technology for intelligent manufacture. Current cutting force sensors exist problems of large volume, complex structure and poor compatibility in practical application, for these problems, a smart cutting tool is proposed in this paper for cutting force measurement. Commercial MEMS (Micro-Electro-Mechanical System) strain gauges with high sensitivity and small size are adopted as transducing element of the smart tool, and a structure optimized cutting tool is fabricated for MEMS strain gauge bonding. Static calibration results show that the developed smart cutting tool is able to measure cutting forces in both X and Y directions, and the cross-interference error is within 3%. Its general accuracy is 3.35% and 3.27% in X and Y directions, and sensitivity is 0.1 mV/N, which is very suitable for measuring small cutting forces in high speed and precision machining. The smart cutting tool is portable and reliable for practical application in CNC machine tool.

A Recommender System in the Cyber Defense Domain

DTIC Science & Technology

2014-03-27

monitoring software is a java based program sending updates to the database on the sensor machine. The host monitoring program gathers information about...3.2.2 Database. A MySQL database located on the sensor machine acts as the storage for the sensors on the network. Snort, Nmap, vulnerability scores, and...machine with the IDS and the recommender is labeled “sensor”. The recommender system code is written in java and compiled using java version 1.6.024

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

PubMed

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-03-04

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

PubMed Central

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-01-01

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications. PMID:26959020

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining

PubMed Central

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-01-01

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing. PMID:27854322

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

PubMed

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-11-16

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

PubMed Central

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; de Jesus Romero-Troncoso, Rene

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node. PMID:22163602

A field programmable gate array-based reconfigurable smart-sensor network for wireless monitoring of new generation computer numerically controlled machines.

PubMed

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; Romero-Troncoso, Rene de Jesus

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node.

[The design and experiment of multi-parameter water quality monitoring microsystem based on MOEMS microspectrometer].

PubMed

Wei, Kang-Lin; Wen, Zhi-Yu; Guo, Jian; Chen, Song-Bo

2012-07-01

Aiming at the monitoring and protecting of water resource environment, a multi-parameter water quality monitoring microsystem based on microspectrometer was put forward in the present paper. The microsystem is mainly composed of MOEMS microspectrometer, flow paths system and embedded measuring & controlling system. It has the functions of self-injecting samples and detection regents, automatic constant temperature, self -stirring, self- cleaning and samples' spectrum detection. The principle prototype machine of the microsystem was developed, and its structure principle was introduced in the paper. Through experiment research, it was proved that the principle prototype machine can rapidly detect quite a few water quality parameters and can meet the demands of on-line water quality monitoring, moreover, the principle prototype machine has strong function expansibility.

Monitoring of diesel engine combustions based on the acoustic source characterisation of the exhaust system

NASA Astrophysics Data System (ADS)

Jiang, J.; Gu, F.; Gennish, R.; Moore, D. J.; Harris, G.; Ball, A. D.

2008-08-01

Acoustic methods are among the most useful techniques for monitoring the condition of machines. However, the influence of background noise is a major issue in implementing this method. This paper introduces an effective monitoring approach to diesel engine combustion based on acoustic one-port source theory and exhaust acoustic measurements. It has been found that the strength, in terms of pressure, of the engine acoustic source is able to provide a more accurate representation of the engine combustion because it is obtained by minimising the reflection effects in the exhaust system. A multi-load acoustic method was then developed to determine the pressure signal when a four-cylinder diesel engine was tested with faults in the fuel injector and exhaust valve. From the experimental results, it is shown that a two-load acoustic method is sufficient to permit the detection and diagnosis of abnormalities in the pressure signal, caused by the faults. This then provides a novel and yet reliable method to achieve condition monitoring of diesel engines even if they operate in high noise environments such as standby power stations and vessel chambers.

Inspection of wear particles in oils by using a fuzzy classifier

NASA Astrophysics Data System (ADS)

Hamalainen, Jari J.; Enwald, Petri

1994-11-01

The reliability of stand-alone machines and larger production units can be improved by automated condition monitoring. Analysis of wear particles in lubricating or hydraulic oils helps diagnosing the wear states of machine parts. This paper presents a computer vision system for automated classification of wear particles. Digitized images from experiments with a bearing test bench, a hydraulic system with an industrial company, and oil samples from different industrial sources were used for algorithm development and testing. The wear particles were divided into four classes indicating different wear mechanisms: cutting wear, fatigue wear, adhesive wear, and abrasive wear. The results showed that the fuzzy K-nearest neighbor classifier utilized gave the same distribution of wear particles as the classification by a human expert.

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users' Feedback, IoT and Machine Learning: A Case Study †.

PubMed

Salamone, Francesco; Belussi, Lorenzo; Currò, Cristian; Danza, Ludovico; Ghellere, Matteo; Guazzi, Giulia; Lenzi, Bruno; Megale, Valentino; Meroni, Italo

2018-05-17

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users' parameters; the machine learning CART method allows to predict the users' profile and the thermal comfort perception respect to the indoor environment.

Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study †

PubMed Central

Currò, Cristian; Danza, Ludovico; Ghellere, Matteo; Guazzi, Giulia; Lenzi, Bruno; Megale, Valentino; Meroni, Italo

2018-01-01

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment. PMID:29772818

Multi-method automated diagnostics of rotating machines

NASA Astrophysics Data System (ADS)

Kostyukov, A. V.; Boychenko, S. N.; Shchelkanov, A. V.; Burda, E. A.

2017-08-01

The automated machinery diagnostics and monitoring systems utilized within the petrochemical plants are an integral part of the measures taken to ensure safety and, as a consequence, the efficiency of these industrial facilities. Such systems are often limited in their functionality due to the specifics of the diagnostic techniques adopted. As the diagnostic techniques applied in each system are limited, and machinery defects can have different physical nature, it becomes necessary to combine several diagnostics and monitoring systems to control various machinery components. Such an approach is inconvenient, since it requires additional measures to bring the diagnostic results in a single view of the technical condition of production assets. In this case, we mean by a production facility a bonded complex of a process unit, a drive, a power source and lines. A failure of any of these components will cause an outage of the production asset, which is unacceptable. The purpose of the study is to test a combined use of vibration diagnostics and partial discharge techniques within the diagnostic systems of enterprises for automated control of the technical condition of rotating machinery during maintenance and at production facilities. The described solutions allow you to control the condition of mechanical and electrical components of rotating machines. It is shown that the functionality of the diagnostics systems can be expanded with minimal changes in technological chains of repair and operation of rotating machinery. Automation of such systems reduces the influence of the human factor on the quality of repair and diagnostics of the machinery.

Conductive ink print on PA66 gear for manufacturing condition monitoring sensors

NASA Astrophysics Data System (ADS)

Futagawa, Shintaro; Iba, Daisuke; Kamimoto, Takahiro; Nakamura, Morimasa; Miura, Nanako; Iizuka, Takashi; Masuda, Arata; Sone, Akira; Moriwaki, Ichiro

2018-03-01

Failures detection of rotating machine elements, such as gears, is an important issue. The purpose of this study was to try to solve this issue by printing conductive ink on gears to manufacture condition-monitoring sensors. In this work, three types of crack detection sensor were designed and the sprayed conductive ink was directly sintered on polyimide (PI) - coated polyamide (PA) 66 gears by laser. The result showed that it was possible to produce narrow circuit lines of the conductive ink including Ag by laser sintering technique and the complex shape sensors on the lateral side of the PA66 gears, module 1.0 mm and tooth number 48. A preliminary operation test was carried out for investigation of the function of the sensors. As a result of the test, the sensors printed in this work should be effective for detecting cracks at tooth root of the gears and will allow for the development of better equipment and detection techniques for health monitoring of gears.

A probabilistic-based approach to monitoring tool wear state and assessing its effect on workpiece quality in nickel-based alloys

NASA Astrophysics Data System (ADS)

Akhavan Niaki, Farbod

The objective of this research is first to investigate the applicability and advantage of statistical state estimation methods for predicting tool wear in machining nickel-based superalloys over deterministic methods, and second to study the effects of cutting tool wear on the quality of the part. Nickel-based superalloys are among those classes of materials that are known as hard-to-machine alloys. These materials exhibit a unique combination of maintaining their strength at high temperature and have high resistance to corrosion and creep. These unique characteristics make them an ideal candidate for harsh environments like combustion chambers of gas turbines. However, the same characteristics that make nickel-based alloys suitable for aggressive conditions introduce difficulties when machining them. High strength and low thermal conductivity accelerate the cutting tool wear and increase the possibility of the in-process tool breakage. A blunt tool nominally deteriorates the surface integrity and damages quality of the machined part by inducing high tensile residual stresses, generating micro-cracks, altering the microstructure or leaving a poor roughness profile behind. As a consequence in this case, the expensive superalloy would have to be scrapped. The current dominant solution for industry is to sacrifice the productivity rate by replacing the tool in the early stages of its life or to choose conservative cutting conditions in order to lower the wear rate and preserve workpiece quality. Thus, monitoring the state of the cutting tool and estimating its effects on part quality is a critical task for increasing productivity and profitability in machining superalloys. This work aims to first introduce a probabilistic-based framework for estimating tool wear in milling and turning of superalloys and second to study the detrimental effects of functional state of the cutting tool in terms of wear and wear rate on part quality. In the milling operation, the mechanisms of tool failure were first identified and, based on the rapid catastrophic failure of the tool, a Bayesian inference method (i.e., Markov Chain Monte Carlo, MCMC) was used for parameter calibration of tool wear using a power mechanistic model. The calibrated model was then used in the state space probabilistic framework of a Kalman filter to estimate the tool flank wear. Furthermore, an on-machine laser measuring system was utilized and fused into the Kalman filter to improve the estimation accuracy. In the turning operation the behavior of progressive wear was investigated as well. Due to the nonlinear nature of wear in turning, an extended Kalman filter was designed for tracking progressive wear, and the results of the probabilistic-based method were compared with a deterministic technique, where significant improvement (more than 60% increase in estimation accuracy) was achieved. To fulfill the second objective of this research in understanding the underlying effects of wear on part quality in cutting nickel-based superalloys, a comprehensive study on surface roughness, dimensional integrity and residual stress was conducted. The estimated results derived from a probabilistic filter were used for finding the proper correlations between wear, surface roughness and dimensional integrity, along with a finite element simulation for predicting the residual stress profile for sharp and worn cutting tool conditions. The output of this research provides the essential information on condition monitoring of the tool and its effects on product quality. The low-cost Hall effect sensor used in this work to capture spindle power in the context of the stochastic filter can effectively estimate tool wear in both milling and turning operations, while the estimated wear can be used to generate knowledge of the state of workpiece surface integrity. Therefore the true functionality and efficiency of the tool in superalloy machining can be evaluated without additional high-cost sensing.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

Computer Simulation Of An In-Process Surface Finish Sensor.

NASA Astrophysics Data System (ADS)

Rakels, Jan H.

1987-01-01

It is generally accepted, that optical methods are the most promising for the in-process measurement of surface finish. These methods have the advantages of being non-contacting and fast data acquisition. Furthermore, these optical instruments can be easily retrofitted on existing machine-tools. In the Micro-Engineering Centre at the University of Warwick, an optical sensor has been developed which can measure the rms roughness, slope and wavelength of turned and precision ground surfaces during machining. The operation of this device is based upon the Kirchhoff-Fresnel diffraction integral. Application of this theory to ideal turned and ground surfaces is straightforward, and indeed the calculated diffraction patterns are in close agreement with patterns produced by an actual optical instrument. Since it is mathematically difficult to introduce real machine-tool behaviour into the diffraction integral, a computer program has been devised, which simulates the operation of the optical sensor. The program produces a diffraction pattern as a graphical output. Comparison between computer generated and actual diffraction patterns of the same surfaces show a high correlation. The main aim of this program is to construct an atlas, which maps known machine-tool errors versus optical diffraction patterns. This atlas can then be used for machine-tool condition diagnostics. It has been found that optical monitoring is very sensitive to minor defects. Therefore machine-tool detoriation can be detected before it is detrimental.

A deviation based assessment methodology for multiple machine health patterns classification and fault detection

NASA Astrophysics Data System (ADS)

Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

2018-01-01

Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

Sensors of vibration and acoustic emission for monitoring of boring with skiving cutters

NASA Astrophysics Data System (ADS)

Shamarin, N. N.; Filippov, A. V.; Podgornyh, O. A.; Filippova, E. O.

2017-01-01

Diagnosing processing system conditions is a key area in automation of modern machinery production. The article presents the results of a preliminary experimental research of the boring process using conventional and skiving cutters under the conditions of the low stiffness processing system. Acoustic emission and vibration sensors are used for cutting process diagnosis. Surface roughness after machining is determined using a laser scanning microscope. As a result, it is found that the use of skiving cutters provides greater stability of the cutting process and lower surface roughness as compared with conventional cutters.

A comparison of temporal artery thermometers with internal blood monitors to measure body temperature during hemodialysis.

PubMed

Lunney, Meaghan; Tonelli, Bronwyn; Lewis, Rachel; Wiebe, Natasha; Thomas, Chandra; MacRae, Jennifer; Tonelli, Marcello

2018-06-14

Thermometers that measure core (internal) body temperature are the gold standard for monitoring temperature. Despite that most modern hemodialysis machines are equipped with an internal blood monitor that measures core body temperature, current practice is to use peripheral thermometers. A better understanding of how peripheral thermometers compare with the dialysis machine thermometer may help guide practice. The study followed a prospective cross-sectional design. Hemodialysis patients were recruited from 2 sites in Calgary, Alberta (April - June 2017). Body temperatures were obtained from peripheral (temporal artery) and dialysis machine thermometers concurrently. Paired t-tests, Bland-Altman plots, and quantile-quantile plots were used to compare measurements from the two devices and to explore potential factors affecting temperature in hemodialysis patients. The mean body temperature of 94 hemodialysis patients measured using the temporal artery thermometer (36.7 °C) was significantly different than the dialysis machine thermometer (36.4 °C); p < 0.001. The mean difference (0.27 °C) appeared to be consistent across average temperature (range: 35.8-37.3 °C). Temperature measured by the temporal artery thermometer was statistically and clinically higher than that measured by the dialysis machine thermometer. Using the dialysis machine to monitor body temperature may result in more accurate readings and is likely to reduce the purchasing and maintenance costs associated with manual temperature readings, as well as easing the workload for dialysis staff.

Research and design of smart grid monitoring control via terminal based on iOS system

NASA Astrophysics Data System (ADS)

Fu, Wei; Gong, Li; Chen, Heli; Pan, Guangji

2017-06-01

Aiming at a series of problems existing in current smart grid monitoring Control Terminal, such as high costs, poor portability, simple monitoring system, poor software extensions, low system reliability when transmitting information, single man-machine interface, poor security, etc., smart grid remote monitoring system based on the iOS system has been designed. The system interacts with smart grid server so that it can acquire grid data through WiFi/3G/4G networks, and monitor each grid line running status, as well as power plant equipment operating conditions. When it occurs an exception in the power plant, incident information can be sent to the user iOS terminal equipment timely, which will provide troubleshooting information to help the grid staff to make the right decisions in a timely manner, to avoid further accidents. Field tests have shown the system realizes the integrated grid monitoring functions, low maintenance cost, friendly interface, high security and reliability, and it possesses certain applicable value.

Design, fabrication, and operation of a test rig for high-speed tapered-roller bearings

NASA Technical Reports Server (NTRS)

Signer, H. R.

1974-01-01

A tapered-roller bearing test machine was designed, fabricated and successfully operated at speeds to 20,000 rpm. Infinitely variable radial loads to 26,690 N (6,000 lbs.) and thrust loads to 53,380 N (12,000 lbs.) can be applied to test bearings. The machine instrumentation proved to have the accuracy and reliability required for parametric bearing performance testing and has the capability of monitoring all programmed test parameters at continuous operation during life testing. This system automatically shuts down a test if any important test parameter deviates from the programmed conditions, or if a bearing failure occurs. A lubrication system was developed as an integral part of the machine, capable of lubricating test bearings by external jets and by means of passages feeding through the spindle and bearing rings into the critical internal bearing surfaces. In addition, provisions were made for controlled oil cooling of inner and outer rings to effect the type of bearing thermal management that is required when testing at high speeds.

A multi-modal approach for activity classification and fall detection

NASA Astrophysics Data System (ADS)

Castillo, José Carlos; Carneiro, Davide; Serrano-Cuerda, Juan; Novais, Paulo; Fernández-Caballero, Antonio; Neves, José

2014-04-01

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

PubMed Central

Markides, Andreas; Skillman, Severin; Acton, Sahr Thomas; Elsaleh, Tarek; Hassanpour, Masoud; Ahrabian, Alireza; Kenny, Mark; Klein, Stuart; Rostill, Helen; Nilforooshan, Ramin; Barnaghi, Payam

2018-01-01

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%. PMID:29723236

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

PubMed

Enshaeifar, Shirin; Zoha, Ahmed; Markides, Andreas; Skillman, Severin; Acton, Sahr Thomas; Elsaleh, Tarek; Hassanpour, Masoud; Ahrabian, Alireza; Kenny, Mark; Klein, Stuart; Rostill, Helen; Nilforooshan, Ramin; Barnaghi, Payam

2018-01-01

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Man-machine cooperation in advanced teleoperation

NASA Technical Reports Server (NTRS)

Fiorini, Paolo; Das, Hari; Lee, Sukhan

1993-01-01

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints.

Design of an automatic production monitoring system on job shop manufacturing

NASA Astrophysics Data System (ADS)

Prasetyo, Hoedi; Sugiarto, Yohanes; Rosyidi, Cucuk Nur

2018-02-01

Every production process requires monitoring system, so the desired efficiency and productivity can be monitored at any time. This system is also needed in the job shop type of manufacturing which is mainly influenced by the manufacturing lead time. Processing time is one of the factors that affect the manufacturing lead time. In a conventional company, the recording of processing time is done manually by the operator on a sheet of paper. This method is prone to errors. This paper aims to overcome this problem by creating a system which is able to record and monitor the processing time automatically. The solution is realized by utilizing electric current sensor, barcode, RFID, wireless network and windows-based application. An automatic monitoring device is attached to the production machine. It is equipped with a touch screen-LCD so that the operator can use it easily. Operator identity is recorded through RFID which is embedded in his ID card. The workpiece data are collected from the database by scanning the barcode listed on its monitoring sheet. A sensor is mounted on the machine to measure the actual machining time. The system's outputs are actual processing time and machine's capacity information. This system is connected wirelessly to a workshop planning application belongs to the firm. Test results indicated that all functions of the system can run properly. This system successfully enables supervisors, PPIC or higher level management staffs to monitor the processing time quickly with a better accuracy.

Application of Electro Chemical Machining for materials used in extreme conditions

NASA Astrophysics Data System (ADS)

Pandilov, Z.

2018-03-01

Electro-Chemical Machining (ECM) is the generic term for a variety of electrochemical processes. ECM is used to machine work pieces from metal and metal alloys irrespective of their hardness, strength or thermal properties, through the anodic dissolution, in aerospace, automotive, construction, medical equipment, micro-systems and power supply industries. The Electro Chemical Machining is extremely suitable for machining of materials used in extreme conditions. General overview of the Electro-Chemical Machining and its application for different materials used in extreme conditions is presented.

76 FR 37838 - Petitions for Modification of Application of Existing Mandatory Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-28

... may include periodic tests of methane levels and limits on the minimum methane concentrations that may...) Methane monitor(s) will be calibrated on the longwall, continuous mining machine, or cutting machine and... petitioner will test for methane with a hand-held methane detector at least every 10 minutes from the time...

Tool Wear Monitoring Using Time Series Analysis

NASA Astrophysics Data System (ADS)

Song, Dong Yeul; Ohara, Yasuhiro; Tamaki, Haruo; Suga, Masanobu

A tool wear monitoring approach considering the nonlinear behavior of cutting mechanism caused by tool wear and/or localized chipping is proposed, and its effectiveness is verified through the cutting experiment and actual turning machining. Moreover, the variation in the surface roughness of the machined workpiece is also discussed using this approach. In this approach, the residual error between the actually measured vibration signal and the estimated signal obtained from the time series model corresponding to dynamic model of cutting is introduced as the feature of diagnosis. Consequently, it is found that the early tool wear state (i.e. flank wear under 40µm) can be monitored, and also the optimal tool exchange time and the tool wear state for actual turning machining can be judged by this change in the residual error. Moreover, the variation of surface roughness Pz in the range of 3 to 8µm can be estimated by the monitoring of the residual error.

Application of the Teager-Kaiser energy operator in bearing fault diagnosis.

PubMed

Henríquez Rodríguez, Patricia; Alonso, Jesús B; Ferrer, Miguel A; Travieso, Carlos M

2013-03-01

Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Method and system for fault accommodation of machines

NASA Technical Reports Server (NTRS)

Goebel, Kai Frank (Inventor); Subbu, Rajesh Venkat (Inventor); Rausch, Randal Thomas (Inventor); Frederick, Dean Kimball (Inventor)

2011-01-01

A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery

NASA Astrophysics Data System (ADS)

Jia, Xiaodong; Zhao, Ming; Di, Yuan; Li, Pin; Lee, Jay

2018-03-01

Sparsity is becoming a more and more important topic in the area of machine learning and signal processing recently. One big family of sparse measures in current literature is the generalized lp /lq norm, which is scale invariant and is widely regarded as normalized lp norm. However, the characteristics of the generalized lp /lq norm are still less discussed and its application to the condition monitoring of rotating devices has been still unexplored. In this study, we firstly discuss the characteristics of the generalized lp /lq norm for sparse optimization and then propose a method of sparse filtering with the generalized lp /lq norm for the purpose of impulsive signature enhancement. Further driven by the trend of industrial big data and the need of reducing maintenance cost for industrial equipment, the proposed sparse filter is customized for vibration signal processing and also implemented on bearing and gearbox for the purpose of condition monitoring. Based on the results from the industrial implementations in this paper, the proposed method has been found to be a promising tool for impulsive feature enhancement, and the superiority of the proposed method over previous methods is also demonstrated.

Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

NASA Astrophysics Data System (ADS)

Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

2017-07-01

This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Matthews, Bryan L.

2011-01-01

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques

NASA Astrophysics Data System (ADS)

Delvecchio, S.; Bonfiglio, P.; Pompoli, F.

2018-01-01

This paper deals with the state-of-the-art strategies and techniques based on vibro-acoustic signals that can monitor and diagnose malfunctions in Internal Combustion Engines (ICEs) under both test bench and vehicle operating conditions. Over recent years, several authors have summarized what is known in critical reviews mainly focused on reciprocating machines in general or on specific signal processing techniques: no attempts to deal with IC engine condition monitoring have been made. This paper first gives a brief summary of the generation of sound and vibration in ICEs in order to place further discussion on fault vibro-acoustic diagnosis in context. An overview of the monitoring and diagnostic techniques described in literature using both vibration and acoustic signals is also provided. Different faulty conditions are described which affect combustion, mechanics and the aerodynamics of ICEs. The importance of measuring acoustic signals, as opposed to vibration signals, is due since the former seem to be more suitable for implementation on on-board monitoring systems in view of their non-intrusive behaviour, capability in simultaneously capturing signatures from several mechanical components and because of the possibility of detecting faults affecting airborne transmission paths. In view of the recent needs of the industry to (-) optimize component structural durability adopting long-life cycles, (-) verify the engine final status at the end of the assembly line and (-) reduce the maintenance costs monitoring the ICE life during vehicle operations, monitoring and diagnosing system requests are continuously growing up. The present review can be considered a useful guideline for test engineers in understanding which types of fault can be diagnosed by using vibro-acoustic signals in sufficient time in both test bench and operating conditions and which transducer and signal processing technique (of which the essential background theory is here reported) could be considered the most reliable and informative to be implemented for the fault in question.

Aspects of structural health and condition monitoring of offshore wind turbines

PubMed Central

Antoniadou, I.; Dervilis, N.; Papatheou, E.; Maguire, A. E.; Worden, K.

2015-01-01

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector. PMID:25583864

Aspects of structural health and condition monitoring of offshore wind turbines.

PubMed

Antoniadou, I; Dervilis, N; Papatheou, E; Maguire, A E; Worden, K

2015-02-28

Wind power has expanded significantly over the past years, although reliability of wind turbine systems, especially of offshore wind turbines, has been many times unsatisfactory in the past. Wind turbine failures are equivalent to crucial financial losses. Therefore, creating and applying strategies that improve the reliability of their components is important for a successful implementation of such systems. Structural health monitoring (SHM) addresses these problems through the monitoring of parameters indicative of the state of the structure examined. Condition monitoring (CM), on the other hand, can be seen as a specialized area of the SHM community that aims at damage detection of, particularly, rotating machinery. The paper is divided into two parts: in the first part, advanced signal processing and machine learning methods are discussed for SHM and CM on wind turbine gearbox and blade damage detection examples. In the second part, an initial exploration of supervisor control and data acquisition systems data of an offshore wind farm is presented, and data-driven approaches are proposed for detecting abnormal behaviour of wind turbines. It is shown that the advanced signal processing methods discussed are effective and that it is important to adopt these SHM strategies in the wind energy sector.

Indirect Tire Monitoring System - Machine Learning Approach

NASA Astrophysics Data System (ADS)

Svensson, O.; Thelin, S.; Byttner, S.; Fan, Y.

2017-10-01

The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an acceptable range over a period of time. These incidents will be notified through the cloud for the operator to analyze for upcoming maintenance decisions.

Spatiotemporal modeling of node temperatures in supercomputers

DOE PAGES

Storlie, Curtis Byron; Reich, Brian James; Rust, William Newton; ...

2016-06-10

Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this andmore » other machine rooms. This paper focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1,600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. Lastly, this same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well.« less

The Modern Integrated Anaesthesia Workstation

PubMed Central

Patil, Vijaya P; Shetmahajan, Madhavi G; Divatia, Jigeeshu V

2013-01-01

Over the years, the conventional anaesthesia machine has evolved into an advanced carestation. The new machines use advanced electronics, software and technology to offer extensive capabilities for ventilation, monitoring, inhaled agent delivery, low-flow anaesthesia and closed-loop anaesthesia. They offer integrated monitoring and recording facilities and seamless integration with anaesthesia information systems. It is possible to deliver tidal volumes accurately and eliminate several hazards associated with the low pressure system and oxygen flush. Appropriate use can result in enhanced safety and ergonomy of anaesthetic delivery and monitoring. However, these workstations have brought in a new set of limitations and potential drawbacks. There are differences in technology and operational principles amongst the new workstations. Understand the principles of operation of these workstations and have a thorough knowledge of the operating manual of the individual machines. PMID:24249877

ANN based Performance Evaluation of BDI for Condition Monitoring of Induction Motor Bearings

NASA Astrophysics Data System (ADS)

Patel, Raj Kumar; Giri, V. K.

2017-06-01

One of the critical parts in rotating machines is bearings and most of the failure arises from the defective bearings. Bearing failure leads to failure of a machine and the unpredicted productivity loss in the performance. Therefore, bearing fault detection and prognosis is an integral part of the preventive maintenance procedures. In this paper vibration signal for four conditions of a deep groove ball bearing; normal (N), inner race defect (IRD), ball defect (BD) and outer race defect (ORD) were acquired from a customized bearing test rig, under four different conditions and three different fault sizes. Two approaches have been opted for statistical feature extraction from the vibration signal. In the first approach, raw signal is used for statistical feature extraction and in the second approach statistical features extracted are based on bearing damage index (BDI). The proposed BDI technique uses wavelet packet node energy coefficients analysis method. Both the features are used as inputs to an ANN classifier to evaluate its performance. A comparison of ANN performance is made based on raw vibration data and data chosen by using BDI. The ANN performance has been found to be fairly higher when BDI based signals were used as inputs to the classifier.

Monitoring osseointegration and developing intelligent systems (Conference Presentation)

NASA Astrophysics Data System (ADS)

Salvino, Liming W.

2017-05-01

Effective monitoring of structural and biological systems is an extremely important research area that enables technology development for future intelligent devices, platforms, and systems. This presentation provides an overview of research efforts funded by the Office of Naval Research (ONR) to establish structural health monitoring (SHM) methodologies in the human domain. Basic science efforts are needed to utilize SHM sensing, data analysis, modeling, and algorithms to obtain the relevant physiological and biological information for human-specific health and performance conditions. This overview of current research efforts is based on the Monitoring Osseointegrated Prosthesis (MOIP) program. MOIP develops implantable and intelligent prosthetics that are directly anchored to the bone of residual limbs. Through real-time monitoring, sensing, and responding to osseointegration of bones and implants as well as interface conditions and environment, our research program aims to obtain individualized actionable information for implant failure identification, load estimation, infection mitigation and treatment, as well as healing assessment. Looking ahead to achieve ultimate goals of SHM, we seek to expand our research areas to cover monitoring human, biological and engineered systems, as well as human-machine interfaces. Examples of such include 1) brainwave monitoring and neurological control, 2) detecting and evaluating brain injuries, 3) monitoring and maximizing human-technological object teaming, and 4) closed-loop setups in which actions can be triggered automatically based on sensors, actuators, and data signatures. Finally, some ongoing and future collaborations across different disciplines for the development of knowledge automation and intelligent systems will be discussed.

Dynamic Data-Driven Prognostics and Condition Monitoring of On-board Electronics

DTIC Science & Technology

2012-08-27

of functionality and accessibility; it is an open language unlike Java or Visual meaning that it is also free. It is also one of the most popular...and C# are able to run without the use of a virtual machine like Java . 4.2.1.5 Implementation For building of an OSA-CBM system, the primer...documentation [7] recommends the following steps: 1. Choose a middleware technology (DCOM, CORBA, Web Services, Java RMI, etc.). 2. Transform OSA-CBM UML

Measurement of the Out-of-Plane Shear Response of Thick Section Composite Materials Using the V-Notched Beam Specimen

DTIC Science & Technology

1993-04-01

to failure at 1.25 mm/min.(.05 in./min.) by a hydraulic, 267 kN (60,000 lb.) capacity, Satec testing machine. Strain output was conditioned through...D.Hoyns 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS( ES ) ’S. PERFORMING ORGANIZATION REPORT NUMBER Naval Surface Warfare Center Carderock Division...Annapolis Detachment CRDKNSWC-SSM-64-92/22 Code 2844/644 9. SPONSORING /MON7ORING AGENCY NAME(S) AND ADDRESS( ES ) 10. SPONSORING /MONITOR INGAGENCY REPORT

Resistance characteristics of innovative eco-fitness equipment: a water buoyancy muscular machine.

PubMed

Chen, Wei-Han; Liu, Ya-Chen; Tai, Hsing-Hao; Liu, Chiang

2018-03-01

This paper propose innovative eco-fitness equipment-a water buoyancy muscular machine (WBM machine) in which resistance is generated by buoyancy and fluid resistance. The resistance characteristics during resistance adjustment and under isometric, concentric, eccentric and isokinetic conditions were investigated and compared to a conventional machine with metal weight plates. The results indicated that the isometric, concentric and eccentric resistances could be adjusted by varying the water volume; the maximum resistances under isometric, concentric and eccentric conditions were 163.8, 338.5 and 140.9 N, respectively. The isometric resistances at different positions remained constant in both machines; however the isometric resistance was lower for WBM machine when at a position corresponding to a 5% total displacement. The WBM machine has lower resistance under eccentric conditions and higher resistance under concentric conditions. Although the conventional machine has an identical trend, the variation was minor (within 4 N). In the WBM machine, the eccentric resistance was approximately 30-45% of the concentric resistance. Concentric resistances increased with an increase in velocity in both machines; however, the eccentric resistances decreased with an increase in velocity. In summary, the WBM machine, a piece of innovative eco-fitness equipment, has unique resistance characteristics and expansibility.

AE Monitoring of Diamond Turned Rapidly Soldified Aluminium 443

NASA Astrophysics Data System (ADS)

Onwuka, G.; Abou-El-Hossein, K.; Mkoko, Z.

2017-05-01

The fast replacement of conventional aluminium with rapidly solidified aluminium alloys has become a noticeable trend in the current manufacturing industries involved in the production of optics and optical molding inserts. This is as a result of the improved performance and durability of rapidly solidified aluminium alloys when compared to conventional aluminium. Melt spinning process is vital for manufacturing rapidly solidified aluminium alloys like RSA 905, RSA 6061 and RSA 443 which are common in the industries today. RSA 443 is a newly developed alloy with few research findings and huge research potential. There is no available literature focused on monitoring the machining of RSA 443 alloys. In this research, Acoustic Emission sensing technique was applied to monitor the single point diamond turning of RSA 443 on an ultrahigh precision lathe machine. The machining process was carried out after careful selection of feed, speed and depths of cut. The monitoring process was achieved with a high sampling data acquisition system using different tools while concurrent measurement of the surface roughness and tool wear were initiated after covering a total feed distance of 13km. An increasing trend of raw AE spikes and peak to peak signal were observed with an increase in the surface roughness and tool wear values. Hence, acoustic emission sensing technique proves to be an effective monitoring method for the machining of RSA 443 alloy.

Haditha General Hospital Under the Economic Support Fund Program Haditha, Iraq

DTIC Science & Technology

2009-06-23

disease from the use of the restrooms. Photos 10 and 11. Heart monitors and defibrillator machines (left) and standing...350 kilometers west of Baghdad, Haditha is a river-side community with an estimated population of 150,000. The hospital, located in the heart of...medical equipment requiring electricity; specifically, several heart monitors and defibrillator machines (Site Photo 10). This equipment appeared

A low cost mid-infrared sensor for on line contamination monitoring of lubricating oils in marine engines

NASA Astrophysics Data System (ADS)

Ben Mohammadi, L.; Kullmann, F.; Holzki, M.; Sigloch, S.; Klotzbuecher, T.; Spiesen, J.; Tommingas, T.; Weismann, P.; Kimber, G.

2010-04-01

The chemical and physical condition of oils in marine engines must be monitored to ensure optimum performance of the engine and to avoid damage by degraded oil not adequately lubricating the engine. Routine monitoring requires expensive laboratory testing and highly skilled analysts. This work describes the adaptation and implementation of a mid infrared (MIR) sensor module for continued oil condition monitoring in two-stroke and four-stroke diesel engines. The developed sensor module will help to reduce costs in oil analysis by eliminating the need to collect and send samples to a laboratory for analysis. The online MIR-Sensor module measures the contamination of oil with water, soot, as well as the degradation indicated by the TBN (Total Base Number) value. For the analysis of water, TBN, and soot in marine engine oils, four spectral regions of interest have been identified. The optical absorption in these bands correlating with the contaminations is measured simultaneously by using a four-field thermopile detector, combined with appropriate bandpass filters. Recording of the MIR-absorption was performed in a transmission mode using a flow-through cell with appropriate path length. Since in this case no spectrometer is required, the sensor including the light source, the flowthrough- cell, and the detector can be realised at low cost and in a very compact manner. The optical configuration of the sensor with minimal component number and signal intensity optimisation at the four-field detector was implemented by using non-sequential ray tracing simulation. The used calibration model was robust enough to predict accurately the value for soot, water, and TBN concentration for two-stroke and four-stroke engine oils. The sensor device is designed for direct installation on the host engine or machine and, therefore, becoming an integral part of the lubrication system. It can also be used as a portable stand-alone system for machine fluid analysis in the field.

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

Database Integrity Monitoring for Synthetic Vision Systems Using Machine Vision and SHADE

NASA Technical Reports Server (NTRS)

Cooper, Eric G.; Young, Steven D.

2005-01-01

In an effort to increase situational awareness, the aviation industry is investigating technologies that allow pilots to visualize what is outside of the aircraft during periods of low-visibility. One of these technologies, referred to as Synthetic Vision Systems (SVS), provides the pilot with real-time computer-generated images of obstacles, terrain features, runways, and other aircraft regardless of weather conditions. To help ensure the integrity of such systems, methods of verifying the accuracy of synthetically-derived display elements using onboard remote sensing technologies are under investigation. One such method is based on a shadow detection and extraction (SHADE) algorithm that transforms computer-generated digital elevation data into a reference domain that enables direct comparison with radar measurements. This paper describes machine vision techniques for making this comparison and discusses preliminary results from application to actual flight data.

A real time status monitor for transistor bank driver power limit resistor in boost injection kicker power supply

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mi, J.; Tan, Y.; Zhang, W.

2011-03-28

For years suffering of Booster Injection Kicker transistor bank driver regulator troubleshooting, a new real time monitor system has been developed. A simple and floating circuit has been designed and tested. This circuit monitor system can monitor the driver regulator power limit resistor status in real time and warn machine operator if the power limit resistor changes values. This paper will mainly introduce the power supply and the new designed monitoring system. This real time resistor monitor circuit shows a useful method to monitor some critical parts in the booster pulse power supply. After two years accelerator operation, it showsmore » that this monitor works well. Previously, we spent a lot of time in booster machine trouble shooting. We will reinstall all 4 PCB into Euro Card Standard Chassis when the power supply system will be updated.« less

A design of the u-health monitoring system using a Nintendo DS game machine.

PubMed

Lee, Sangjoon; Kim, Jinkwon; Kim, Jungkuk; Lee, Myoungho

2009-01-01

In this paper, we used the hand held type a Nintendo DS Game Machine for consisting of a u-Health Monitoring system. This system is consists of four parts. Biosignal acquire device is the first. The Second is a wireless sensor network device. The third is a wireless base-station for connecting internet network. Displaying units are the last part which were a personal computer and a Nintendo DS game machine. The bio-signal measurement device among the four parts the u-health monitoring system can acquire 7-channels data which have 3-channels ECG(Electrocardiogram), 3-axis accelerometer and tilting sensor data. Acquired data connect up the internet network throughout the wireless sensor network and a base-station. In the experiment, we concurrently display the bio-signals on to a monitor of personal computer and LCD of a Nintendo DS using wireless internet protocol and those monitoring devices placed off to the one side an office building. The result of the experiment, this proposed system effectively can transmit patient's biosignal data as a long time and a long distance. This suggestion of the u-health monitoring system need to operate in the ambulance, general hospitals and geriatric institutions as a u-health monitoring device.

A New mHealth Communication Framework for Use in Wearable WBANs and Mobile Technologies

PubMed Central

Hamida, Sana Tmar-Ben; Hamida, Elyes Ben; Ahmed, Beena

2015-01-01

Driven by the development of biomedical sensors and the availability of high mobile bandwidth, mobile health (mHealth) systems are now offering a wider range of new services. This revolution makes the idea of in-home health monitoring practical and provides the opportunity for assessment in “real-world” environments producing more ecologically valid data. In the field of insomnia diagnosis, for example, it is now possible to offer patients wearable sleep monitoring systems which can be used in the comfort of their homes over long periods of time. The recorded data collected from body sensors can be sent to a remote clinical back-end system for analysis and assessment. Most of the research on sleep reported in the literature mainly looks into how to automate the analysis of the sleep data and does not address the problem of the efficient encoding and secure transmissions of the collected health data. This article reviews the key enabling communication technologies and research challenges for the design of efficient mHealth systems. An end-to-end mHealth system architecture enabling the remote assessment and monitoring of patient's sleep disorders is then proposed and described as a case study. Finally, various mHealth data serialization formats and machine-to-machine (M2M) communication protocols are evaluated and compared under realistic operating conditions. PMID:25654718

Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

NASA Astrophysics Data System (ADS)

Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

2013-07-01

Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

The research of knitting needle status monitoring setup

NASA Astrophysics Data System (ADS)

Liu, Lu; Liao, Xiao-qing; Zhu, Yong-kang; Yang, Wei; Zhang, Pei; Zhao, Yong-kai; Huang, Hui-jie

2013-09-01

In textile production, quality control and testing is the key to ensure the process and improve the efficiency. Defect of the knitting needles is the main factor affecting the quality of the appearance of textiles. Defect detection method based on machine vision and image processing technology is universal. This approach does not effectively identify the defect generated by damaged knitting needles and raise the alarm. We developed a knitting needle status monitoring setup using optical imaging, photoelectric detection and weak signal processing technology to achieve real-time monitoring of weaving needles' position. Depending on the shape of the knitting needle, we designed a kind of Glass Optical Fiber (GOF) light guides with a rectangular port used for transmission of the signal light. To be able to capture the signal of knitting needles accurately, we adopt a optical 4F system which has better imaging quality and simple structure and there is a rectangle image on the focal plane after the system. When a knitting needle passes through position of the rectangle image, the reflected light from needle surface will back to the GOF light guides along the same optical system. According to the intensity of signals, the computer control unit distinguish that the knitting needle is broken or curving. The experimental results show that this system can accurately detect the broken needles and the curving needles on the knitting machine in operating condition.

A new mHealth communication framework for use in wearable WBANs and mobile technologies.

PubMed

Hamida, Sana Tmar-Ben; Hamida, Elyes Ben; Ahmed, Beena

2015-02-03

Driven by the development of biomedical sensors and the availability of high mobile bandwidth, mobile health (mHealth) systems are now offering a wider range of new services. This revolution makes the idea of in-home health monitoring practical and provides the opportunity for assessment in "real-world" environments producing more ecologically valid data. In the field of insomnia diagnosis, for example, it is now possible to offer patients wearable sleep monitoring systems which can be used in the comfort of their homes over long periods of time. The recorded data collected from body sensors can be sent to a remote clinical back-end system for analysis and assessment. Most of the research on sleep reported in the literature mainly looks into how to automate the analysis of the sleep data and does not address the problem of the efficient encoding and secure transmissions of the collected health data. This article reviews the key enabling communication technologies and research challenges for the design of efficient mHealth systems. An end-to-end mHealth system architecture enabling the remote assessment and monitoring of patient's sleep disorders is then proposed and described as a case study. Finally, various mHealth data serialization formats and machine-to-machine (M2M) communication protocols are evaluated and compared under realistic operating conditions.

Global Positioning System (GPS) and Geographic Information System (GIS) analysis of mobile harvesting equipment and sediment delivery to streams during forest harvest operations on steep terrain: Experimental design

Treesearch

Daniel Bowker; Jeff Stringer; Chris Barton; Songlin Fei

2011-01-01

Sediment mobilized by forest harvest machine traffic contributes substantially to the degradation of headwater stream systems. This study monitored forest harvest machine traffic to analyze how it affects sediment delivery to stream channels. Harvest machines were outfitted with global positioning system (GPS) dataloggers, recording machine movements and working status...

Analysis of NREL Cold-Drink Vending Machines for Energy Savings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deru, M.; Torcellini, P.; Bottom, K.

NREL Staff, as part of Sustainable NREL, an initiative to improve the overall energy and environmental performance of the lab, decided to control how its vending machines used energy. The cold-drink vending machines across the lab were analyzed for potential energy savings opportunities. This report gives the monitoring and the analysis of two energy conservation measures applied to the cold-drink vending machines at NREL.

Assessment of Surface Area Characteristics of Dental Implants with Gradual Bioactive Surface Treatment

NASA Astrophysics Data System (ADS)

Czan, Andrej; Babík, Ondrej; Miklos, Matej; Záušková, Lucia; Mezencevová, Viktória

2017-10-01

Since most of the implant surface is in direct contact with bone tissue, shape and integrity of said surface has great influence on successful osseointegration. Among other characteristics that predetermine titanium of different grades of pureness as ideal biomaterial, titanium shows high mechanical strength making precise miniature machining increasingly difficult. Current titanium-based implants are often anodized due to colour coding. This anodized layer has important functional properties for right usage and also bio-compatibility of dental implants. Physical method of anodizing and usage of anodizing mediums has a significant influence on the surface quality and itself functionality. However, basic requirement of the dental implant with satisfactory properties is quality of machined surface before anodizing. Roughness, for example, is factor affecting of time length of anodizing operation and so whole productivity. The paper is focused on monitoring of surface and area characteristics, such as roughness or surface integrity after different cutting conditions of miniature machining of dental implants and their impact on suitability for creation of satisfactory anodized layer with the correct biocompatible functional properties.

Method and apparatus for improved wire saw slurry

DOEpatents

Costantini, Michael A.; Talbott, Jonathan A.; Chandra, Mohan; Prasad, Vishwanath; Caster, Allison; Gupta, Kedar P.; Leyvraz, Philippe

2000-09-05

A slurry recycle process for use in free-abrasive machining operations such as for wire saws used in wafer slicing of ingots, where the used slurry is separated into kerf-rich and abrasive-rich components, and the abrasive-rich component is reconstituted into a makeup slurry. During the process, the average particle size of the makeup slurry is controlled by monitoring the condition of the kerf and abrasive components and making necessary adjustments to the separating force and dwell time of the separator apparatus. Related pre-separator and post separator treatments, and feedback of one or the other separator slurry output components for mixing with incoming used slurry and recirculation through the separator, provide further effectiveness and additional control points in the process. The kerf-rich component is eventually or continually removed; the abrasive-rich component is reconstituted into a makeup slurry with a controlled, average particle size such that the products of the free-abrasive machining method using the recycled slurry process of the invention are of consistent high quality with less TTV deviation from cycle to cycle for a prolonged period or series of machining operations.

Hardware assisted hypervisor introspection.

PubMed

Shi, Jiangyong; Yang, Yuexiang; Tang, Chuan

2016-01-01

In this paper, we introduce hypervisor introspection, an out-of-box way to monitor the execution of hypervisors. Similar to virtual machine introspection which has been proposed to protect virtual machines in an out-of-box way over the past decade, hypervisor introspection can be used to protect hypervisors which are the basis of cloud security. Virtual machine introspection tools are usually deployed either in hypervisor or in privileged virtual machines, which might also be compromised. By utilizing hardware support including nested virtualization, EPT protection and #BP, we are able to monitor all hypercalls belongs to the virtual machines of one hypervisor, include that of privileged virtual machine and even when the hypervisor is compromised. What's more, hypercall injection method is used to simulate hypercall-based attacks and evaluate the performance of our method. Experiment results show that our method can effectively detect hypercall-based attacks with some performance cost. Lastly, we discuss our furture approaches of reducing the performance cost and preventing the compromised hypervisor from detecting the existence of our introspector, in addition with some new scenarios to apply our hypervisor introspection system.

Washing machine usage in remote aboriginal communities.

PubMed

Lloyd, C R

1998-10-01

The use of washing machines was investigated in two remote Aboriginal communities in the Anangu Pitjantjatjara homelands. The aim was to look both at machine reliability and to investigate the health aspect of washing clothes. A total of 39 machines were inspected for wear and component reliability every three months over a one-year period. Of these, 10 machines were monitored in detail for water consumption, hours of use and cycles of operation. The machines monitored were Speed Queen model EA2011 (7 kg washing load) commercial units. The field survey results suggested a high rate of operation of the machines with an average of around 1,100 washing cycles per year (range 150 and 2,300 cycles per year). The results were compared with available figures for the average Australian household. A literature survey, to ascertain the health outcomes relating to washing clothes and bedding, confirmed that washing machines are efficient at removal of bacteria from clothes and bedding but suggested that recontamination of clothing after washing often negated the prior removal. High temperature washing (> 60 degrees C) appeared to be advantageous from a health perspective. With regards to larger organisms, while dust mites and body lice transmission between people would probably be decreased by washing clothes, scabies appeared to be mainly transmitted by body contact and thus transmission would be only marginally decreased by the use of washing machines.

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

PubMed Central

Pérez-López, Carlos; Català, Andreu; Moreno Arostegui, Joan M.; Cabestany, Joan; Bayés, Àngels; Alcaine, Sheila; Mestre, Berta; Prats, Anna; Crespo, M. Cruz; Counihan, Timothy J.; Browne, Patrick; Quinlan, Leo R.; ÓLaighin, Gearóid; Sweeney, Dean; Lewy, Hadas; Azuri, Joseph; Vainstein, Gabriel; Annicchiarico, Roberta; Costa, Alberto; Rodríguez-Molinero, Alejandro

2017-01-01

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy. PMID:28199357

Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

NASA Astrophysics Data System (ADS)

Johnson, Nicholas E.; Bonczak, Bartosz; Kontokosta, Constantine E.

2018-07-01

The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36-0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68-0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors.

Artificial Neural Network Based Fault Diagnostics of Rolling Element Bearings Using Time-Domain Features

NASA Astrophysics Data System (ADS)

Samanta, B.; Al-Balushi, K. R.

2003-03-01

A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between -1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines.

Interferometric correction system for a numerically controlled machine

DOEpatents

Burleson, Robert R.

1978-01-01

An interferometric correction system for a numerically controlled machine is provided to improve the positioning accuracy of a machine tool, for example, for a high-precision numerically controlled machine. A laser interferometer feedback system is used to monitor the positioning of the machine tool which is being moved by command pulses to a positioning system to position the tool. The correction system compares the commanded position as indicated by a command pulse train applied to the positioning system with the actual position of the tool as monitored by the laser interferometer. If the tool position lags the commanded position by a preselected error, additional pulses are added to the pulse train applied to the positioning system to advance the tool closer to the commanded position, thereby reducing the lag error. If the actual tool position is leading in comparison to the commanded position, pulses are deleted from the pulse train where the advance error exceeds the preselected error magnitude to correct the position error of the tool relative to the commanded position.

An evolutionary sensor approach for self-organizing production chains

NASA Astrophysics Data System (ADS)

Mocan, M.; Gillich, E. V.; Mituletu, I. C.; Korka, Z. I.

2018-01-01

Industry 4.0 is the actual great step in industrial progress. Convergence of industrial equipment with the power of advanced computing and analysis, low-cost sensing, and new connecting technologies are presumed to bring unexpected advancements in automation, flexibility, and efficiency. In this context, sensors ensure information regarding three essential areas: the number of processed elements, the quality of production and the condition of tools and equipment. To obtain this valuable information, the data resulted from a sensor has to be firstly processed and afterward used by the different stakeholders. If machines are linked together, this information can be employed to organize the production chain with few or without human intervention. We describe here the implementation of a sensor in a milling machine that is part of a simple production chain, capable of providing information regarding the number of manufactured pieces. It is used by the other machines in the production chain, in order to define the type and number of pieces to be manufactured by them and/or to set optimal parameters for their working regime. Secondly, the information achieved by monitoring the machine and manufactured piece dynamic behavior is used to evaluate the product quality. This information is used to warn about the need of maintenance, being transmitted to the specialized department. It is also transmitted to the central unit, in order to reorganize the production by involving other machines or by reconsidering the manufacturing regime of the existing machines. A special attention is drawn on analyzing and classifying the signals acquired via optical sensor from simulated processes.

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

NASA Astrophysics Data System (ADS)

Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

2018-01-01

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information

PubMed Central

Cai, Gaigai; Chen, Xuefeng; Li, Bing; Chen, Baojia; He, Zhengjia

2012-01-01

The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools. PMID:23201980

Performance Monitoring Of A Computer Numerically Controlled (CNC) Lathe Using Pattern Recognition Techniques

NASA Astrophysics Data System (ADS)

Daneshmend, L. K.; Pak, H. A.

1984-02-01

On-line monitoring of the cutting process in CNC lathe is desirable to ensure unattended fault-free operation in an automated environment. The state of the cutting tool is one of the most important parameters which characterises the cutting process. Direct monitoring of the cutting tool or workpiece is not feasible during machining. However several variables related to the state of the tool can be measured on-line. A novel monitoring technique is presented which uses cutting torque as the variable for on-line monitoring. A classifier is designed on the basis of the empirical relationship between cutting torque and flank wear. The empirical model required by the on-line classifier is established during an automated training cycle using machine vision for off-line direct inspection of the tool.

Expert System Software

NASA Technical Reports Server (NTRS)

1989-01-01

C Language Integrated Production System (CLIPS) is a software shell for developing expert systems is designed to allow research and development of artificial intelligence on conventional computers. Originally developed by Johnson Space Center, it enables highly efficient pattern matching. A collection of conditions and actions to be taken if the conditions are met is built into a rule network. Additional pertinent facts are matched to the rule network. Using the program, E.I. DuPont de Nemours & Co. is monitoring chemical production machines; California Polytechnic State University is investigating artificial intelligence in computer aided design; Mentor Graphics has built a new Circuit Synthesis system, and Brooke and Brooke, a law firm, can determine which facts from a file are most important.

Detection and classification of alarm threshold violations in condition monitoring systems working in highly varying operational conditions

NASA Astrophysics Data System (ADS)

Strączkiewicz, M.; Barszcz, T.; Jabłoński, A.

2015-07-01

All commonly used condition monitoring systems (CMS) enable defining alarm thresholds that enhance efficient surveillance and maintenance of dynamic state of machinery. The thresholds are imposed on the measured values such as vibration-based indicators, temperature, pressure, etc. For complex machinery such as wind turbine (WT) the total number of thresholds might be counted in hundreds multiplied by the number of operational states. All the parameters vary not only due to possible machinery malfunctions, but also due to changes in operating conditions and these changes are typically much stronger than the former ones. Very often, such a behavior may lead to hundreds of false alarms. Therefore, authors propose a novel approach based on parameterized description of the threshold violation. For this purpose the novelty and severity factors are introduced. The first parameter refers to the time of violation occurrence while the second one describes the impact of the indicator-increase to the entire machine. Such approach increases reliability of the CMS by providing the operator with the most useful information of the system events. The idea of the procedure is presented on a simulated data similar to those from a wind turbine.

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

Amplifying human ability through autonomics and machine learning in IMPACT

NASA Astrophysics Data System (ADS)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

PubMed

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

2017-07-28

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform.

PubMed

Ben Salem, Samira; Bacha, Khmais; Chaari, Abdelkader

2012-09-01

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

PubMed Central

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang

2017-01-01

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. PMID:28788099

Origin of acoustic emission produced during single point machining

NASA Astrophysics Data System (ADS)

Heiple, C. R.; Carpenter, S. H.; Armentrout, D. L.

1991-05-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emission produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent.

Interpreting Medical Information Using Machine Learning and Individual Conditional Expectation.

PubMed

Nohara, Yasunobu; Wakata, Yoshifumi; Nakashima, Naoki

2015-01-01

Recently, machine-learning techniques have spread many fields. However, machine-learning is still not popular in medical research field due to difficulty of interpreting. In this paper, we introduce a method of interpreting medical information using machine learning technique. The method gave new explanation of partial dependence plot and individual conditional expectation plot from medical research field.

Machine learning and new vital signs monitoring in civilian en route care: A systematic review of the literature and future implications for the military.

PubMed

Liu, Nehemiah T; Salinas, Jose

2016-11-01

Although air transport medical services are today an integral part of trauma systems in most developed countries, to date, there are no reviews on recent innovations in civilian en route care. The purpose of this systematic review was to identify potential machine learning and new vital signs monitoring technologies in civilian en route care that could help close civilian and military capability gaps in monitoring and the early detection and treatment of various trauma injuries. MEDLINE, the Cochrane Database of Systematic Reviews, and citation review of relevant primary and review articles were searched for studies involving civilian en route care, air medical transport, and technologies from January 2005 to November 2015. Data were abstracted on study design, population, year, sponsors, innovation category, details of technologies, and outcomes. Thirteen observational studies involving civilian medical transport met inclusion criteria. Studies either focused on machine learning and software algorithms (n = 5), new vital signs monitoring (n = 6), or both (n = 2). Innovations involved continuous digital acquisition of physiologic data and parameter extraction. Importantly, all studies (n = 13) demonstrated improved outcomes where applicable and potential use during civilian and military en route care. However, almost all studies required further validation in prospective and/or randomized controlled trials. Potential machine learning technologies and monitoring of novel vital signs such as heart rate variability and complexity in civilian en route care could help enhance en route care for our nation's war fighters. In a complex global environment, they could potentially fill capability gaps such as monitoring and the early detection and treatment of various trauma injuries. However, the impact of these innovations and technologies will require further validation before widespread acceptance and prehospital use. Systematic review, level V.

Model-based chatter stability prediction and detection for the turning of a flexible workpiece

NASA Astrophysics Data System (ADS)

Lu, Kaibo; Lian, Zisheng; Gu, Fengshou; Liu, Hunju

2018-02-01

Machining long slender workpieces still presents a technical challenge on the shop floor due to their low stiffness and damping. Regenerative chatter is a major hindrance in machining processes, reducing the geometric accuracies and dynamic stability of the cutting system. This study has been motivated by the fact that chatter occurrence is generally in relation to the cutting position in straight turning of slender workpieces, which has seldom been investigated comprehensively in literature. In the present paper, a predictive chatter model of turning a tailstock supported slender workpiece considering the cutting position change during machining is explored. Based on linear stability analysis and stiffness distribution at different cutting positions along the workpiece, the effect of the cutting tool movement along the length of the workpiece on chatter stability is studied. As a result, an entire stability chart for a single cutting pass is constructed. Through this stability chart the critical cutting condition and the chatter onset location along the workpiece in a turning operation can be estimated. The difference between the predicted tool locations and the experimental results was within 9% at high speed cutting. Also, on the basis of the predictive model the dynamic behavior during chatter that when chatter arises at some cutting location it will continue for a period of time until another specified location is arrived at, can be inferred. The experimental observation is in good agreement with the theoretical inference. In chatter detection respect, besides the delay strategy and overlap processing technique, a relative threshold algorithm is proposed to detect chatter by comparing the spectrum and variance of the acquired acceleration signals with the reference saved during stable cutting. The chatter monitoring method has shown reliability for various machining conditions.

MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

NASA Technical Reports Server (NTRS)

Albayrak, Arif; Wei, Jennifer; Petrenko, Maksym; Lary, David; Leptoukh, Gregory

2011-01-01

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

Anaesthesia is one of the few sub-specialties of medicine, which has quickly adapted technology to improve patient safety. This application of technology can be seen in patient monitoring, advances in anaesthesia machines, intubating devices, ultrasound for visualisation of nerves and vessels, etc., Anaesthesia machines have come a long way in the last 100 years, the improvements being driven both by patient safety as well as functionality and economy of use. Incorporation of safety features in anaesthesia machines and ensuring that a proper check of the machine is done before use on a patient ensures patient safety. This review will trace all the present safety features in the machine and their evolution. PMID:24249880

Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train - A contemporary survey

NASA Astrophysics Data System (ADS)

Uma Maheswari, R.; Umamaheswari, R.

2017-02-01

Condition Monitoring System (CMS) substantiates potential economic benefits and enables prognostic maintenance in wind turbine-generator failure prevention. Vibration Monitoring and Analysis is a powerful tool in drive train CMS, which enables the early detection of impending failure/damage. In variable speed drives such as wind turbine-generator drive trains, the vibration signal acquired is of non-stationary and non-linear. The traditional stationary signal processing techniques are inefficient to diagnose the machine faults in time varying conditions. The current research trend in CMS for drive-train focuses on developing/improving non-linear, non-stationary feature extraction and fault classification algorithms to improve fault detection/prediction sensitivity and selectivity and thereby reducing the misdetection and false alarm rates. In literature, review of stationary signal processing algorithms employed in vibration analysis is done at great extent. In this paper, an attempt is made to review the recent research advances in non-linear non-stationary signal processing algorithms particularly suited for variable speed wind turbines.

Wireless and Low-Weight Technologies: Advanced Medical Assistance During a Cave Rescue: A Case Report.

PubMed

Petrucci, Emiliano; Pizzi, Barbara; Scimia, Paolo; Conti, Giuseppe; Di Carlo, Stefano; Santini, Antonella; Fusco, Pierfrancesco

2018-06-01

Trauma care in cave rescue is a unique situation that requires an advanced and organized approach with medical and technical assistance because of the extreme environmental conditions and logistical factors. In caving accidents, the most common injuries involve lower limbs. We describe an advanced medical rescue performed by the Italian Corpo Nazionale del Soccorso Alpino e Speleologico, in which extended focused assessment with sonography for trauma and an ultrasound-guided adductor canal block were performed on a patient with a knee distortion directly in the cave. The rescue team inside the cave shared data on patient monitoring and the ultrasound scanning in real time with rescuers at the entrance, using a video conference powered by the new Ermes system. The use of handheld, battery-powered, low-weight, multiparametric monitors, ultrasound machines, and digital data transmission systems could ensure complete medical assistance in harsh environmental conditions such as those found in a cave. Copyright © 2018 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

NASA Astrophysics Data System (ADS)

Zhang, Bin; Deng, Congying; Zhang, Yi

2018-03-01

Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

NASA Astrophysics Data System (ADS)

Golafshan, Reza; Yuce Sanliturk, Kenan

2016-03-01

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy.

PubMed

Hannemann, Jan; Poorter, Hendrik; Usadel, Björn; Bläsing, Oliver E; Finck, Alex; Tardieu, Francois; Atkin, Owen K; Pons, Thijs; Stitt, Mark; Gibon, Yves

2009-09-01

Data mining depends on the ability to access machine-readable metadata that describe genotypes, environmental conditions, and sampling times and strategy. This article presents Xeml Lab. The Xeml Interactive Designer provides an interactive graphical interface at which complex experiments can be designed, and concomitantly generates machine-readable metadata files. It uses a new eXtensible Mark-up Language (XML)-derived dialect termed XEML. Xeml Lab includes a new ontology for environmental conditions, called Xeml Environment Ontology. However, to provide versatility, it is designed to be generic and also accepts other commonly used ontology formats, including OBO and OWL. A review summarizing important environmental conditions that need to be controlled, monitored and captured as metadata is posted in a Wiki (http://www.codeplex.com/XeO) to promote community discussion. The usefulness of Xeml Lab is illustrated by two meta-analyses of a large set of experiments that were performed with Arabidopsis thaliana during 5 years. The first reveals sources of noise that affect measurements of metabolite levels and enzyme activities. The second shows that Arabidopsis maintains remarkably stable levels of sugars and amino acids across a wide range of photoperiod treatments, and that adjustment of starch turnover and the leaf protein content contribute to this metabolic homeostasis.

CMM Interim Check (U)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Montano, Joshua Daniel

2015-03-23

Coordinate Measuring Machines (CMM) are widely used in industry, throughout the Nuclear Weapons Complex and at Los Alamos National Laboratory (LANL) to verify part conformance to design definition. Calibration cycles for CMMs at LANL are predominantly one year in length. Unfortunately, several nonconformance reports have been generated to document the discovery of a certified machine found out of tolerance during a calibration closeout. In an effort to reduce risk to product quality two solutions were proposed – shorten the calibration cycle which could be costly, or perform an interim check to monitor the machine’s performance between cycles. The CMM interimmore » check discussed makes use of Renishaw’s Machine Checking Gauge. This off-the-shelf product simulates a large sphere within a CMM’s measurement volume and allows for error estimation. Data was gathered, analyzed, and simulated from seven machines in seventeen different configurations to create statistical process control run charts for on-the-floor monitoring.« less

Virtual Mission Operations of Remote Sensors With Rapid Access To and From Space

NASA Technical Reports Server (NTRS)

Ivancic, William D.; Stewart, Dave; Walke, Jon; Dikeman, Larry; Sage, Steven; Miller, Eric; Northam, James; Jackson, Chris; Taylor, John; Lynch, Scott;

Holter monitor (24h)

MedlinePlus

... please enable JavaScript. A Holter monitor is a machine that continuously records the heart’s rhythms. The monitor is worn for 24 to 48 hours during normal activity. How the Test is Performed Electrodes (small conducting patches) are stuck ...

Effect of the Machined Surfaces of AISI 4337 Steel to Cutting Conditions on Dry Machining Lathe

NASA Astrophysics Data System (ADS)

Rahim, Robbi; Napid, Suhardi; Hasibuan, Abdurrozzaq; Rahmah Sibuea, Siti; Yusmartato, Y.

2018-04-01

The objective of the research is to obtain a cutting condition which has a good chance of realizing dry machining concept on AISI 4337 steel material by studying surface roughness, microstructure and hardness of machining surface. The data generated from the experiment were then processed and analyzed using the standard Taguchi method L9 (34) orthogonal array. Testing of dry and wet machining used surface test and micro hardness test for each of 27 test specimens. The machining results of the experiments showed that average surface roughness (Raavg) was obtained at optimum cutting conditions when VB 0.1 μm, 0.3 μm and 0.6 μm respectively 1.467 μm, 2.133 μm and 2,800 μm fo r dry machining while which was carried out by wet machining the results obtained were 1,833 μm, 2,667 μm and 3,000 μm. It can be concluded that dry machining provides better surface quality of machinery results than wet machining. Therefore, dry machining is a good choice that may be realized in the manufacturing and automotive industries.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

PROCEDURES FOR ACCURATE PRODUCTION OF COLOR IMAGES FROM SATELLITE OR AIRCRAFT MULTISPECTRAL DIGITAL DATA.

USGS Publications Warehouse

Duval, Joseph S.

1985-01-01

Because the display and interpretation of satellite and aircraft remote-sensing data make extensive use of color film products, accurate reproduction of the color images is important. To achieve accurate color reproduction, the exposure and chemical processing of the film must be monitored and controlled. By using a combination of sensitometry, densitometry, and transfer functions that control film response curves, all of the different steps in the making of film images can be monitored and controlled. Because a sensitometer produces a calibrated exposure, the resulting step wedge can be used to monitor the chemical processing of the film. Step wedges put on film by image recording machines provide a means of monitoring the film exposure and color balance of the machines.

30 CFR 75.342 - Methane monitors.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors. 75.342 Section 75.342 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.342 Methane monitors. (a)(1) MSHA approved methane monitors shall be installed on all face cutting machines, continuous miners, longwall face...

30 CFR 75.342 - Methane monitors.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors. 75.342 Section 75.342 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.342 Methane monitors. (a)(1) MSHA approved methane monitors shall be installed on all face cutting machines, continuous miners, longwall face...

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

Over the past few decades the induction machine has been chosen for many applications due to its structural simplicity and low manufacturing cost. However, modest torque density and control challenges have motivated researchers to find alternative machines. The permanent magnet synchronous machine has been viewed as one of the alternatives because it features higher torque density for a given loss than the induction machine. However, the assembly and permanent magnet material cost, along with safety under fault conditions, have been concerns for this class of machine. An alternative machine type, namely the asymmetrical reluctance machine, is proposed in this work. Since the proposed machine is of the reluctance machine type, it possesses desirable feature, such as near absence of rotor losses, low assembly cost, low no-load rotational losses, modest torque ripple, and rather benign fault conditions. Through theoretical analysis performed herein, it is shown that this machine has a higher torque density for a given loss than typical reluctance machines, although not as high as the permanent magnet machines. Thus, the asymmetrical reluctance machine is a viable and advantageous machine alternative where the use of permanent magnet machines are undesirable.

Beam measurements using visible synchrotron light at NSLS2 storage ring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, Weixing, E-mail: chengwx@bnl.gov; Bacha, Bel; Singh, Om

2016-07-27

Visible Synchrotron Light Monitor (SLM) diagnostic beamline has been designed and constructed at NSLS2 storage ring, to characterize the electron beam profile at various machine conditions. Due to the excellent alignment, SLM beamline was able to see the first visible light when beam was circulating the ring for the first turn. The beamline has been commissioned for the past year. Besides a normal CCD camera to monitor the beam profile, streak camera and gated camera are used to measure the longitudinal and transverse profile to understand the beam dynamics. Measurement results from these cameras will be presented in this paper.more » A time correlated single photon counting system (TCSPC) has also been setup to measure the single bunch purity.« less

30 CFR 57.22308 - Methane monitors (III mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (III mines). 57.22308 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22308 Methane monitors (III mines). (a) Methane monitors shall be installed on continuous mining machines and longwall mining systems. (b) The...

30 CFR 57.22308 - Methane monitors (III mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (III mines). 57.22308 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22308 Methane monitors (III mines). (a) Methane monitors shall be installed on continuous mining machines and longwall mining systems. (b) The...

Efficient forced vibration reanalysis method for rotating electric machines

NASA Astrophysics Data System (ADS)

Saito, Akira; Suzuki, Hiromitsu; Kuroishi, Masakatsu; Nakai, Hideo

2015-01-01

Rotating electric machines are subject to forced vibration by magnetic force excitation with wide-band frequency spectrum that are dependent on the operating conditions. Therefore, when designing the electric machines, it is inevitable to compute the vibration response of the machines at various operating conditions efficiently and accurately. This paper presents an efficient frequency-domain vibration analysis method for the electric machines. The method enables the efficient re-analysis of the vibration response of electric machines at various operating conditions without the necessity to re-compute the harmonic response by finite element analyses. Theoretical background of the proposed method is provided, which is based on the modal reduction of the magnetic force excitation by a set of amplitude-modulated standing-waves. The method is applied to the forced response vibration of the interior permanent magnet motor at a fixed operating condition. The results computed by the proposed method agree very well with those computed by the conventional harmonic response analysis by the FEA. The proposed method is then applied to the spin-up test condition to demonstrate its applicability to various operating conditions. It is observed that the proposed method can successfully be applied to the spin-up test conditions, and the measured dominant frequency peaks in the frequency response can be well captured by the proposed approach.

What happened to the TSM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brezovec, D.

1983-11-01

A new coal mining machine that was going to pull some 40 million tons of coal from the Appalachian coalfields by 1986 has had more than its share of start-up problems. The machine, known as the Thin Seam Miner (TSM), is a $2.7-million auger-type mining machine that is designed to bore 220 ft into new or abandoned highwalls (CA 5/82 p. 106). Gamma-ray sensors located near the continuous drum miner-type cutter head monitor for rock and other sensors monitor for methane. The machines are designed to produce about 425 tons per shift from a 36-in.-thick coal seam. The machines weremore » introduced officially to the American coal industry at a luncheon Aug. 19, 1981, in a ballroom at the Lexington, Ky., Hyatt Regency Hotel. At the luncheon, some 200 coal industry executives and others sipped champagne and listened to glowing reports of how 24 of the machines would produce 2.2 million tons of coal by the end of 1981 and 64 of the machines would produce 6.6 million tons by the end of 1982. The machines would be built in Holland by RijnSchelde-Verolme (RSV), a major Dutch shipbuilder, and managed in the United States by Advanced Coal Management (ACM), a company formed for the purpose by James D. Stacy, a colorful, cigar-smoking stock car owner whose experience in the coal business dated from only the mid-1970s.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Angers, Crystal Plume; Bottema, Ryan; Buckley, Les

Purpose: Treatment unit uptime statistics are typically used to monitor radiation equipment performance. The Ottawa Hospital Cancer Centre has introduced the use of Quality Control (QC) test success as a quality indicator for equipment performance and overall health of the equipment QC program. Methods: Implemented in 2012, QATrack+ is used to record and monitor over 1100 routine machine QC tests each month for 20 treatment and imaging units ( http://qatrackplus.com/ ). Using an SQL (structured query language) script, automated queries of the QATrack+ database are used to generate program metrics such as the number of QC tests executed and themore » percentage of tests passing, at tolerance or at action. These metrics are compared against machine uptime statistics already reported within the program. Results: Program metrics for 2015 show good correlation between pass rate of QC tests and uptime for a given machine. For the nine conventional linacs, the QC test success rate was consistently greater than 97%. The corresponding uptimes for these units are better than 98%. Machines that consistently show higher failure or tolerance rates in the QC tests have lower uptimes. This points to either poor machine performance requiring corrective action or to problems with the QC program. Conclusions: QATrack+ significantly improves the organization of QC data but can also aid in overall equipment management. Complimenting machine uptime statistics with QC test metrics provides a more complete picture of overall machine performance and can be used to identify areas of improvement in the machine service and QC programs.« less

Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers

NASA Astrophysics Data System (ADS)

Devillez, Arnaud; Dudzinski, Daniel

2007-01-01

Today the knowledge of a process is very important for engineers to find optimal combination of control parameters warranting productivity, quality and functioning without defects and failures. In our laboratory, we carry out research in the field of high speed machining with modelling, simulation and experimental approaches. The aim of our investigation is to develop a software allowing the cutting conditions optimisation to limit the number of predictive tests, and the process monitoring to prevent any trouble during machining operations. This software is based on models and experimental data sets which constitute the knowledge of the process. In this paper, we deal with the problem of vibrations occurring during a machining operation. These vibrations may cause some failures and defects to the process, like workpiece surface alteration and rapid tool wear. To measure on line the tool micro-movements, we equipped a lathe with a specific instrumentation using eddy current sensors. Obtained signals were correlated with surface finish and a signal processing algorithm was used to determine if a test is stable or unstable. Then, a fuzzy classification method was proposed to classify the tests in a space defined by the width of cut and the cutting speed. Finally, it was shown that the fuzzy classification takes into account of the measurements incertitude to compute the stability limit or stability lobes of the process.

An investigation of the effects of measurement noise in the use of instantaneous angular speed for machine diagnosis

NASA Astrophysics Data System (ADS)

Gu, Fengshou; Yesilyurt, Isa; Li, Yuhua; Harris, Georgina; Ball, Andrew

2006-08-01

In order to discriminate small changes for early fault diagnosis of rotating machines, condition monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder. From this signal model and analysis, optimal configurations for IAS data collection are addressed for high accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried out on an electric induction motor driving system using IAS measurement. The diagnosis results show that using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in the frequency range of interest, validating the model and algorithm developed. Moreover, the results demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of motor rotor bar defects and shaft misalignment.

Microbiological quality of ice and ice machines used in food establishments.

PubMed

Hampikyan, Hamparsun; Bingol, Enver Baris; Cetin, Omer; Colak, Hilal

2017-06-01

The ice used in the food industry has to be safe and the water used in ice production should have the quality of drinking water. The consumption of contaminated ice directly or indirectly may be a vehicle for transmission of pathogenic bacteria to humans producing outbreaks of gastrointestinal diseases. The objective of this study was to monitor the microbiological quality of ice, the water used in producing ice and the hygienic conditions of ice making machines in various food enterprises. Escherichia coli was detected in seven (6.7%) ice and 23 (21.9%) ice chest samples whereas E. coli was negative in all examined water samples. Psychrophilic bacteria were detected in 83 (79.0%) of 105 ice chest and in 68 (64.7%) of 105 ice samples, whereas Enterococci were detected only in 13 (12.4%) ice samples. Coliforms were detected in 13 (12.4%) water, 71 (67.6%) ice chest and 54 (51.4%) ice samples. In order to improve the microbiological quality of ice, the maintenance, cleaning and disinfecting of ice machines should be carried out effectively and periodically. Also, high quality water should be used for ice production.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

Origin of acoustic emission produced during single point machining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiple, C.R,.; Carpenter, S.H.; Armentrout, D.L.

1991-01-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emissionmore » produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent. 21 refs., 19 figs., 4 tabs.« less

Machinability of experimental Ti-Ag alloys.

PubMed

Kikuchi, Masafumi; Takahashi, Masatoshi; Okuno, Osamu

2008-03-01

This study investigated the machinability of experimental Ti-Ag alloys (5, 10, 20, and 30 mass% Ag) as a new dental titanium alloy candidate for CAD/CAM use. The alloys were slotted with a vertical milling machine and carbide square end mills under two cutting conditions. Machinability was evaluated through cutting force using a three-component force transducer fixed on the table of the milling machine. The horizontal cutting force of the Ti-Ag alloys tended to decrease as the concentration of silver increased. Values of the component of the horizontal cutting force perpendicular to the feed direction for Ti-20% Ag and Ti-30% Ag were more than 20% lower than those for titanium under both cutting conditions. Alloying with silver significantly improved the machinability of titanium in terms of cutting force under the present cutting conditions.

Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

NASA Astrophysics Data System (ADS)

Li, Lin; Tao, Jian-Feng; Yu, Hai-Dong; Huang, Yi-Xiang; Liu, Cheng-Liang

2017-11-01

The gripper cylinder that provides braced force for Tunnel Boring Machine (TBM) might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition (EMD) method is established for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiffness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the characteristic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylinder based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

Highly Stretchable Core-Sheath Fibers via Wet-Spinning for Wearable Strain Sensors.

PubMed

Tang, Zhenhua; Jia, Shuhai; Wang, Fei; Bian, Changsheng; Chen, Yuyu; Wang, Yonglin; Li, Bo

2018-02-21

Lightweight, stretchable, and wearable strain sensors have recently been widely studied for the development of health monitoring systems, human-machine interfaces, and wearable devices. Herein, highly stretchable polymer elastomer-wrapped carbon nanocomposite piezoresistive core-sheath fibers are successfully prepared using a facile and scalable one-step coaxial wet-spinning assembly approach. The carbon nanotube-polymeric composite core of the stretchable fiber is surrounded by an insulating sheath, similar to conventional cables, and shows excellent electrical conductivity with a low percolation threshold (0.74 vol %). The core-sheath elastic fibers are used as wearable strain sensors, exhibiting ultra-high stretchability (above 300%), excellent stability (>10 000 cycles), fast response, low hysteresis, and good washability. Furthermore, the piezoresistive core-sheath fiber possesses bending-insensitiveness and negligible torsion-sensitive properties, and the strain sensing performance of piezoresistive fibers maintains a high degree of stability under harsh conditions. On the basis of this high level of performance, the fiber-shaped strain sensor can accurately detect both subtle and large-scale human movements by embedding it in gloves and garments or by directly attaching it to the skin. The current results indicate that the proposed stretchable strain sensor has many potential applications in health monitoring, human-machine interfaces, soft robotics, and wearable electronics.

Exposure to Free-Play Modes in Simulated Online Gaming Increases Risk-Taking in Monetary Gambling.

PubMed

Frahn, Tahnee; Delfabbro, Paul; King, Daniel L

2015-12-01

This study examined the behavioral effects of practice modes in simulated slot machine gambling. A sample of 128 participants predominantly aged 18-24 years were randomly allocated to 1 of 4 pre-exposure conditions: control (no practice), standard 90% return to player, inflated return to player and inflated return with pop-up messages. Participants in all conditions engaged in monetary gambling using a realistic online simulation of a slot machine. As predicted, the results showed that those players exposed to inflated or 'profit' demonstration modes placed significantly higher bets in the real-play mode as compared to the other groups. However, the groups did not differ in relation to how long they persisted in the real-play mode. Pop-up messages had no significant effect on monetary gambling behavior. The results of this study confirm that exposure to inflated practice or "demo" modes lead to short-term increases in risk-taking. These findings highlight the need for careful regulation and monitoring of internet gambling sites, as well as further research on the potential risks of simulated gambling activities for vulnerable segments of the gambling population.

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Dynamic species classification of microorganisms across time, abiotic and biotic environments—A sliding window approach

PubMed Central

Griffiths, Jason I.; Fronhofer, Emanuel A.; Garnier, Aurélie; Seymour, Mathew; Altermatt, Florian; Petchey, Owen L.

2017-01-01

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology. PMID:28472193

Machine tool locator

DOEpatents

Hanlon, John A.; Gill, Timothy J.

2001-01-01

Machine tools can be accurately measured and positioned on manufacturing machines within very small tolerances by use of an autocollimator on a 3-axis mount on a manufacturing machine and positioned so as to focus on a reference tooling ball or a machine tool, a digital camera connected to the viewing end of the autocollimator, and a marker and measure generator for receiving digital images from the camera, then displaying or measuring distances between the projection reticle and the reference reticle on the monitoring screen, and relating the distances to the actual position of the autocollimator relative to the reference tooling ball. The images and measurements are used to set the position of the machine tool and to measure the size and shape of the machine tool tip, and examine cutting edge wear. patent

30 CFR 57.22306 - Methane monitors (I-A mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (I-A mines). 57.22306 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22306 Methane monitors (I-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, and on loading...

30 CFR 57.22307 - Methane monitors (II-A mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (II-A mines). 57.22307 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22307 Methane monitors (II-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, bench and face...

30 CFR 57.22309 - Methane monitors (V-A mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (V-A mines). 57.22309 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22309 Methane monitors (V-A mines). (a) Methane monitors shall be installed on continuous mining machines used in or beyond the last open crosscut...

30 CFR 57.22306 - Methane monitors (I-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (I-A mines). 57.22306 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22306 Methane monitors (I-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, and on loading...

30 CFR 57.22307 - Methane monitors (II-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (II-A mines). 57.22307 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22307 Methane monitors (II-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, bench and face...

30 CFR 57.22309 - Methane monitors (V-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (V-A mines). 57.22309 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22309 Methane monitors (V-A mines). (a) Methane monitors shall be installed on continuous mining machines used in or beyond the last open crosscut...

Implementing Machine Learning in Radiology Practice and Research.

PubMed

Kohli, Marc; Prevedello, Luciano M; Filice, Ross W; Geis, J Raymond

2017-04-01

The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

Final Report: Enabling Exascale Hardware and Software Design through Scalable System Virtualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Patrick G.

2015-02-01

In this grant, we enhanced the Palacios virtual machine monitor to increase its scalability and suitability for addressing exascale system software design issues. This included a wide range of research on core Palacios features, large-scale system emulation, fault injection, perfomrance monitoring, and VMM extensibility. This research resulted in large number of high-impact publications in well-known venues, the support of a number of students, and the graduation of two Ph.D. students and one M.S. student. In addition, our enhanced version of the Palacios virtual machine monitor has been adopted as a core element of the Hobbes operating system under active DOE-fundedmore » research and development.« less

40 CFR 60.175 - Monitoring of operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Standards of Performance for Primary Zinc... monitor and record the opacity of gases discharged into the atmosphere from any sintering machine. The... volume. (i) The continuous monitoring system performance evaluation required under § 60.13(c) shall be...

Positional reference system for ultraprecision machining

DOEpatents

Arnold, Jones B.; Burleson, Robert R.; Pardue, Robert M.

1982-01-01

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of position interferometers and part contour description data inputs to calculate error components for each axis of movement and output them to corresponding axis drives with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, J.B.; Burleson, R.R.; Pardue, R.M.

1980-09-12

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of positions interferometers and part contour description data input to calculate error components for each axis of movement and output them to corresponding axis driven with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Structural Health Monitoring with Fiber Bragg Grating and Piezo Arrays

NASA Technical Reports Server (NTRS)

Black, Richard J.; Faridian, Ferey; Moslehi, Behzad; Sotoudeh, Vahid

2012-01-01

Structural health monitoring (SHM) is one of the most important tools available for the maintenance, safety, and integrity of aerospace structural systems. Lightweight, electromagnetic-interference- immune, fiber-optic sensor-based SHM will play an increasing role in more secure air transportation systems. Manufacturers and maintenance personnel have pressing needs for significantly improving safety and reliability while providing for lower inspection and maintenance costs. Undetected or untreated damage may grow and lead to catastrophic structural failure. Damage can originate from the strain/stress history of the material, imperfections of domain boundaries in metals, delamination in multi-layer materials, or the impact of machine tools in the manufacturing process. Damage can likewise develop during service life from wear and tear, or under extraordinary circumstances such as with unusual forces, temperature cycling, or impact of flying objects. Monitoring and early detection are key to preventing a catastrophic failure of structures, especially when these are expected to perform near their limit conditions.

Apparatus for monitoring crystal growth

DOEpatents

Sachs, Emanual M.

1981-01-01

A system and method are disclosed for monitoring the growth of a crystalline body from a liquid meniscus in a furnace. The system provides an improved human/machine interface so as to reduce operator stress, strain and fatigue while improving the conditions for observation and control of the growing process. The system comprises suitable optics for forming an image of the meniscus and body wherein the image is anamorphic so that the entire meniscus can be viewed with good resolution in both the width and height dimensions. The system also comprises a video display for displaying the anamorphic image. The video display includes means for enhancing the contrast between any two contrasting points in the image. The video display also comprises a signal averager for averaging the intensity of at least one preselected portions of the image. The value of the average intensity, can in turn be utilized to control the growth of the body. The system and method are also capable of observing and monitoring multiple processes.

Method of monitoring crystal growth

DOEpatents

Sachs, Emanual M.

1982-01-01

A system and method are disclosed for monitoring the growth of a crystalline body from a liquid meniscus in a furnace. The system provides an improved human/machine interface so as to reduce operator stress, strain and fatigue while improving the conditions for observation and control of the growing process. The system comprises suitable optics for forming an image of the meniscus and body wherein the image is anamorphic so that the entire meniscus can be viewed with good resolution in both the width and height dimensions. The system also comprises a video display for displaying the anamorphic image. The video display includes means for enhancing the contrast between any two contrasting points in the image. The video display also comprises a signal averager for averaging the intensity of at least one preselected portions of the image. The value of the average intensity, can in turn be utilized to control the growth of the body. The system and method are also capable of observing and monitoring multiple processes.

Accelerometer Method and Apparatus for Integral Display and Control Functions

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1996-01-01

Method and apparatus for detecting mechanical vibrations and outputting a signal in response thereto. Art accelerometer package having integral display and control functions is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine conditions over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase in amplitude over a selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated.

A New Real - Time Fault Detection Methodology for Systems Under Test. Phase 1

NASA Technical Reports Server (NTRS)

Johnson, Roger W.; Jayaram, Sanjay; Hull, Richard A.

1998-01-01

The purpose of this research is focussed on the identification/demonstration of critical technology innovations that will be applied to various applications viz. Detection of automated machine Health Monitoring (BM, real-time data analysis and control of Systems Under Test (SUT). This new innovation using a High Fidelity Dynamic Model-based Simulation (BFDMS) approach will be used to implement a real-time monitoring, Test and Evaluation (T&E) methodology including the transient behavior of the system under test. The unique element of this process control technique is the use of high fidelity, computer generated dynamic models to replicate the behavior of actual Systems Under Test (SUT). It will provide a dynamic simulation capability that becomes the reference truth model, from which comparisons are made with the actual raw/conditioned data from the test elements.

Accelerometer Method and Apparatus for Integral Display and Control Functions

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1998-01-01

Method and apparatus for detecting mechanical vibrations and outputting a signal in response thereto is discussed. An accelerometer package having integral display and control functions is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine conditions over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase in amplitude over a selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated.

In-situ acoustic signature monitoring in additive manufacturing processes

NASA Astrophysics Data System (ADS)

Koester, Lucas W.; Taheri, Hossein; Bigelow, Timothy A.; Bond, Leonard J.; Faierson, Eric J.

2018-04-01

Additive manufacturing is a rapidly maturing process for the production of complex metallic, ceramic, polymeric, and composite components. The processes used are numerous, and with the complex geometries involved this can make quality control and standardization of the process and inspection difficult. Acoustic emission measurements have been used previously to monitor a number of processes including machining and welding. The authors have identified acoustic signature measurement as a potential means of monitoring metal additive manufacturing processes using process noise characteristics and those discrete acoustic emission events characteristic of defect growth, including cracks and delamination. Results of acoustic monitoring for a metal additive manufacturing process (directed energy deposition) are reported. The work investigated correlations between acoustic emissions and process noise with variations in machine state and deposition parameters, and provided proof of concept data that such correlations do exist.

Three-dimensional dynamic deformation monitoring using a laser-scanning system

NASA Astrophysics Data System (ADS)

Al-Hanbali, Nedal N.; Teskey, William F.

1994-10-01

Non-contact dynamic deformation monitoring (e.g. with a laser scanning system) is very useful in monitoring changes in alignment and changes in size and shape of coupled operating machines. If relative movements between coupled operating machines are large, excessive wear in the machines or unplanned shutdowns due to machinery failure will occur. The purpose of non-contact dynamic deformation monitoring is to identify the causes of large movements and point to remedial action that can be taken to prevent them. The laser scanning system is a laser-based 3D vision system. The system-technique is based on an auto- synchronized triangulation scanning scheme. The system provides accurate, fast, and reliable 3D measurements and can measure objects between 0.5 m to 100 m with a field of view of 40 degree(s) X 50 degree(s). The system is flexible in terms of providing control over the scanned area and depth. The system also provides the user with the intensity image in addition to the depth coded image. This paper reports on the preliminary testing of this system to monitor surface movements and target (point) movements. The monitoring resolution achieved for an operating motorized alignment test rig in the lab was 1 mm for surface movements and 0.50 m for target movements. Raw data manipulation, local calibration, and the method of relating measurements to control points will be discussed. Possibilities for improving the resolution and recommendations for future development will also be presented.

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

PubMed

Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

2017-10-01

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. To develop an automated stroke-classification system to help quantify hitting load in tennis. Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.

Accelerometer method and apparatus for integral display and control functions

NASA Astrophysics Data System (ADS)

Bozeman, Richard J., Jr.

1992-06-01

Vibration analysis has been used for years to provide a determination of the proper functioning of different types of machinery, including rotating machinery and rocket engines. A determination of a malfunction, if detected at a relatively early stage in its development, will allow changes in operating mode or a sequenced shutdown of the machinery prior to a total failure. Such preventative measures result in less extensive and/or less expensive repairs, and can also prevent a sometimes catastrophic failure of equipment. Standard vibration analyzers are generally rather complex, expensive, and of limited portability. They also usually result in displays and controls being located remotely from the machinery being monitored. Consequently, a need exists for improvements in accelerometer electronic display and control functions which are more suitable for operation directly on machines and which are not so expensive and complex. The invention includes methods and apparatus for detecting mechanical vibrations and outputting a signal in response thereto. The apparatus includes an accelerometer package having integral display and control functions. The accelerometer package is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine condition over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase over the selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated. The benefits of a vibration recording and monitoring system with controls and displays readily mountable on the machinery being monitored and having capabilities described will be appreciated by those working in the art.

Accelerometer method and apparatus for integral display and control functions

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1992-01-01

Vibration analysis has been used for years to provide a determination of the proper functioning of different types of machinery, including rotating machinery and rocket engines. A determination of a malfunction, if detected at a relatively early stage in its development, will allow changes in operating mode or a sequenced shutdown of the machinery prior to a total failure. Such preventative measures result in less extensive and/or less expensive repairs, and can also prevent a sometimes catastrophic failure of equipment. Standard vibration analyzers are generally rather complex, expensive, and of limited portability. They also usually result in displays and controls being located remotely from the machinery being monitored. Consequently, a need exists for improvements in accelerometer electronic display and control functions which are more suitable for operation directly on machines and which are not so expensive and complex. The invention includes methods and apparatus for detecting mechanical vibrations and outputting a signal in response thereto. The apparatus includes an accelerometer package having integral display and control functions. The accelerometer package is suitable for mounting upon the machinery to be monitored. Display circuitry provides signals to a bar graph display which may be used to monitor machine condition over a period of time. Control switches may be set which correspond to elements in the bar graph to provide an alert if vibration signals increase over the selected trip point. The circuitry is shock mounted within the accelerometer housing. The method provides for outputting a broadband analog accelerometer signal, integrating this signal to produce a velocity signal, integrating and calibrating the velocity signal before application to a display driver, and selecting a trip point at which a digitally compatible output signal is generated. The benefits of a vibration recording and monitoring system with controls and displays readily mountable on the machinery being monitored and having capabilities described will be appreciated by those working in the art.

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

PubMed

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

A defect-driven diagnostic method for machine tool spindles

PubMed Central

Vogl, Gregory W.; Donmez, M. Alkan

2016-01-01

Simple vibration-based metrics are, in many cases, insufficient to diagnose machine tool spindle condition. These metrics couple defect-based motion with spindle dynamics; diagnostics should be defect-driven. A new method and spindle condition estimation device (SCED) were developed to acquire data and to separate system dynamics from defect geometry. Based on this method, a spindle condition metric relying only on defect geometry is proposed. Application of the SCED on various milling and turning spindles shows that the new approach is robust for diagnosing the machine tool spindle condition. PMID:28065985

A comparison of numerical and machine-learning modeling of soil water content with limited input data

NASA Astrophysics Data System (ADS)

Karandish, Fatemeh; Šimůnek, Jiří

2016-12-01

Soil water content (SWC) is a key factor in optimizing the usage of water resources in agriculture since it provides information to make an accurate estimation of crop water demand. Methods for predicting SWC that have simple data requirements are needed to achieve an optimal irrigation schedule, especially for various water-saving irrigation strategies that are required to resolve both food and water security issues under conditions of water shortages. Thus, a two-year field investigation was carried out to provide a dataset to compare the effectiveness of HYDRUS-2D, a physically-based numerical model, with various machine-learning models, including Multiple Linear Regressions (MLR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), for simulating time series of SWC data under water stress conditions. SWC was monitored using TDRs during the maize growing seasons of 2010 and 2011. Eight combinations of six, simple, independent parameters, including pan evaporation and average air temperature as atmospheric parameters, cumulative growth degree days (cGDD) and crop coefficient (Kc) as crop factors, and water deficit (WD) and irrigation depth (In) as crop stress factors, were adopted for the estimation of SWCs in the machine-learning models. Having Root Mean Square Errors (RMSE) in the range of 0.54-2.07 mm, HYDRUS-2D ranked first for the SWC estimation, while the ANFIS and SVM models with input datasets of cGDD, Kc, WD and In ranked next with RMSEs ranging from 1.27 to 1.9 mm and mean bias errors of -0.07 to 0.27 mm, respectively. However, the MLR models did not perform well for SWC forecasting, mainly due to non-linear changes of SWCs under the irrigation process. The results demonstrated that despite requiring only simple input data, the ANFIS and SVM models could be favorably used for SWC predictions under water stress conditions, especially when there is a lack of data. However, process-based numerical models are undoubtedly a better choice for predicting SWCs with lower uncertainties when required data are available, and thus for designing water saving strategies for agriculture and for other environmental applications requiring estimates of SWCs.

30 CFR 27.24 - Power-shutoff component.

Code of Federal Regulations, 2010 CFR

2010-07-01

... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.24 Power... the machine or equipment when actuated by the methane detector at a methane concentration of 2.0... actuated by the methane detector, cause a control circuit to shut down the machine or equipment on which it...

Real Time Network Monitoring and Reporting System

ERIC Educational Resources Information Center

Massengale, Ricky L., Sr.

2009-01-01

With the ability of modern system developers to develop intelligent programs that allows machines to learn, modify and evolve themselves, current trends of reactionary methods to detect and eradicate malicious software code from infected machines is proving to be too costly. Addressing malicious software after an attack is the current methodology…

40 CFR 60.185 - Monitoring of operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Standards of Performance for Primary Lead... reverberatory furnace, or sintering machine discharge end. The span of this system shall be set at 80 to 100... discharged into the atmosphere from any sintering machine, electric furnace or converter subject to § 60.183...

WISESight : a multispectral smart video-track intrusion monitor.

DOT National Transportation Integrated Search

2015-05-01

International Electronic Machines : Corporation (IEM) developed, tested, and : validated a unique smart video-based : intrusion monitoring system for use at : highway-rail grade crossings. The system : used both thermal infrared (IR) and : visible/ne...

Musical feedback during exercise machine workout enhances mood

PubMed Central

Fritz, Thomas H.; Halfpaap, Johanna; Grahl, Sophia; Kirkland, Ambika; Villringer, Arno

2013-01-01

Music making has a number of beneficial effects for motor tasks compared to passive music listening. Given that recent research suggests that high energy musical activities elevate positive affect more strongly than low energy musical activities, we here investigated a recent method that combined music making with systematically increasing physiological arousal by exercise machine workout. We compared mood and anxiety after two exercise conditions on non-cyclical exercise machines, one with passive music listening and the other with musical feedback (where participants could make music with the exercise machines). The results showed that agency during exercise machine workout (an activity we previously labeled jymmin – a cross between jammin and gym) had an enhancing effect on mood compared to workout with passive music listening. Furthermore, the order in which the conditions were presented mediated the effect of musical agency for this subscale when participants first listened passively, the difference in mood between the two conditions was greater, suggesting that a stronger increase in hormone levels (e.g., endorphins) during the active condition may have caused the observed effect. Given an enhanced mood after training with musical feedback compared to passively listening to the same type of music during workout, the results suggest that exercise machine workout with musical feedback (jymmin) makes the act of exercise machine training more desirable. PMID:24368905

A Fault Recognition System for Gearboxes of Wind Turbines

NASA Astrophysics Data System (ADS)

Yang, Zhiling; Huang, Haiyue; Yin, Zidong

2017-12-01

Costs of maintenance and loss of power generation caused by the faults of wind turbines gearboxes are the main components of operation costs for a wind farm. Therefore, the technology of condition monitoring and fault recognition for wind turbines gearboxes is becoming a hot topic. A condition monitoring and fault recognition system (CMFRS) is presented for CBM of wind turbines gearboxes in this paper. The vibration signals from acceleration sensors at different locations of gearbox and the data from supervisory control and data acquisition (SCADA) system are collected to CMFRS. Then the feature extraction and optimization algorithm is applied to these operational data. Furthermore, to recognize the fault of gearboxes, the GSO-LSSVR algorithm is proposed, combining the least squares support vector regression machine (LSSVR) with the Glowworm Swarm Optimization (GSO) algorithm. Finally, the results show that the fault recognition system used in this paper has a high rate for identifying three states of wind turbines’ gears; besides, the combination of date features can affect the identifying rate and the selection optimization algorithm presented in this paper can get a pretty good date feature subset for the fault recognition.

Intelligent approach to prognostic enhancements of diagnostic systems

NASA Astrophysics Data System (ADS)

Vachtsevanos, George; Wang, Peng; Khiripet, Noppadon; Thakker, Ash; Galie, Thomas R.

2001-07-01

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

Validation assessment of shoreline extraction on medium resolution satellite image

NASA Astrophysics Data System (ADS)

Manaf, Syaifulnizam Abd; Mustapha, Norwati; Sulaiman, Md Nasir; Husin, Nor Azura; Shafri, Helmi Zulhaidi Mohd

2017-10-01

Monitoring coastal zones helps provide information about the conditions of the coastal zones, such as erosion or accretion. Moreover, monitoring the shorelines can help measure the severity of such conditions. Such measurement can be performed accurately by using Earth observation satellite images rather than by using traditional ground survey. To date, shorelines can be extracted from satellite images with a high degree of accuracy by using satellite image classification techniques based on machine learning to identify the land and water classes of the shorelines. In this study, the researchers validated the results of extracted shorelines of 11 classifiers using a reference shoreline provided by the local authority. Specifically, the validation assessment was performed to examine the difference between the extracted shorelines and the reference shorelines. The research findings showed that the SVM Linear was the most effective image classification technique, as evidenced from the lowest mean distance between the extracted shoreline and the reference shoreline. Furthermore, the findings showed that the accuracy of the extracted shoreline was not directly proportional to the accuracy of the image classification.

Comparison of portable and conventional ultrasound imaging in spinal curvature measurement

NASA Astrophysics Data System (ADS)

Yan, Christina; Tabanfar, Reza; Kempston, Michael; Borschneck, Daniel; Ungi, Tamas; Fichtinger, Gabor

2016-03-01

PURPOSE: In scoliosis monitoring, tracked ultrasound has been explored as a safer imaging alternative to traditional radiography. The use of ultrasound in spinal curvature measurement requires identification of vertebral landmarks, but bones have reduced visibility in ultrasound imaging and high quality ultrasound machines are often expensive and not portable. In this work, we investigate the image quality and measurement accuracy of a low cost and portable ultrasound machine in comparison to a standard ultrasound machine in scoliosis monitoring. METHODS: Two different kinds of ultrasound machines were tested on three human subjects, using the same position tracker and software. Spinal curves were measured in the same reference coordinate system using both ultrasound machines. Lines were defined by connecting two symmetric landmarks identified on the left and right transverse process of the same vertebrae, and spinal curvature was defined as the transverse process angle between two such lines, projected on the coronal plane. RESULTS: Three healthy volunteers were scanned by both ultrasound configurations. Three experienced observers localized transverse processes as skeletal landmarks and obtained transverse process angles in images obtained from both ultrasounds. The mean difference per transverse process angle measured was 3.00 +/-2.1°. 94% of transverse processes visualized in the Sonix Touch were also visible in the Telemed. Inter-observer error in the Telemed was 4.5° and 4.3° in the Sonix Touch. CONCLUSION: Price, convenience and accessibility suggest the Telemed to be a viable alternative in scoliosis monitoring, however further improvements in measurement protocol and image noise reduction must be completed before implementing the Telemed in the clinical setting.

Principles of control automation of soil compacting machine operating mechanism

NASA Astrophysics Data System (ADS)

Anatoly Fedorovich, Tikhonov; Drozdov, Anatoly

2018-03-01

The relevance of the qualitative compaction of soil bases in the erection of embankment and foundations in building and structure construction is given.The quality of the compactible gravel and sandy soils provides the bearing capability and, accordingly, the strength and durability of constructed buildings.It has been established that the compaction quality depends on many external actions, such as surface roughness and soil moisture; granulometry, chemical composition and degree of elasticity of originalfilled soil for compaction.The analysis of technological processes of soil bases compaction of foreign and domestic information sources showed that the solution of such important problem as a continuous monitoring of soil compaction actual degree in the process of machine operation carry out only with the use of modern means of automation. An effective vibrodynamic method of gravel and sand material sealing for the building structure foundations for various applications was justified and suggested.The method of continuous monitoring the soil compaction by measurement of the amplitudes and frequencies of harmonic oscillations on the compactible surface was determined, which allowed to determine the basic elements of facilities of soil compacting machine monitoring system of operating, etc. mechanisms: an accelerometer, a bandpass filter, a vibro-harmonics, an on-board microcontroller. Adjustable parameters have been established to improve the soil compaction degree and the soil compacting machine performance, and the adjustable parameter dependences on the overall indexhave been experimentally determined, which is the soil compaction degree.A structural scheme of automatic control of the soil compacting machine control mechanism and theoperation algorithm has been developed.

On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

NASA Astrophysics Data System (ADS)

Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

2017-11-01

Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

Quantification of mammalian tumor cell state plasticity with digital holographic cytometry

NASA Astrophysics Data System (ADS)

Hejna, Miroslav; Jorapur, Aparna; Zhang, Yuntian; Song, Jun S.; Judson, Robert L.

2018-02-01

Individual cells within isogenic tumor populations can exhibit distinct cellular morphologies, behaviors, and molecular profiles. Cell state plasticity refers to the propensity of a cell to transition between these different morphologies and behaviors. Elevation of cell state plasticity is thought to contribute to critical stages in tumor evolution, including metastatic dissemination and acquisition of therapeutic resistance. However, methods for quantifying general plasticity in mammalian cells remain limited. Working with a HoloMonitor M4 digital holographic cytometry platform, we have established a machine learning-based pipeline for high accuracy and label-free classification of adherent cells. We use twenty-six morphological and optical density-derived features for label-free identification of cell state in heterogeneous cultures. The system is housed completely within a mammalian cell incubator, permitting the monitoring of changes in cell state over time. Here we present an application of our approach for studying cell state plasticity. Human melanoma cell lines of known metastatic potential were monitored in standard growth conditions. The rate of feature change was quantified for each individual cell in the populations. We observed that cells of higher metastatic potential exhibited more rapid fluctuation of cell state in homeostatic conditions. The approach we demonstrate will be advantageous for further investigations into the factors that influence cell state plasticity.

Automatic monitoring of the alignment and wear of vibration welding equipment

DOEpatents

Spicer, John Patrick; Cai, Wayne W.; Chakraborty, Debejyo; Mink, Keith

2017-05-23

A vibration welding system includes vibration welding equipment having a welding horn and anvil, a host machine, a check station, and a welding robot. At least one displacement sensor is positioned with respect to one of the welding equipment and the check station. The robot moves the horn and anvil via an arm to the check station, when a threshold condition is met, i.e., a predetermined amount of time has elapsed or a predetermined number of welds have been completed. The robot moves the horn and anvil to the check station, activates the at least one displacement sensor, at the check station, and determines a status condition of the welding equipment by processing the received signals. The status condition may be one of the alignment of the vibration welding equipment and the wear or degradation of the vibration welding equipment.

Application of electrolytic in-process dressing for high-efficiency grinding of ceramic parts. Research activities 1995--96

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bandyopadhyay, B.P.

1997-02-01

The application of Electrolytic In-Process Dressing (ELID) for highly efficient and stable grinding of ceramic parts is discussed. This research was performed at the Institute of Physical and Chemical Research (RIKEN), Tokyo, Japan, June 1995 through August 1995. Experiments were conducted using a vertical machining center. The silicon nitride work material, of Japanese manufacture and supplied in the form of a rectangular block, was clamped to a vice which was firmly fixed on the base of a strain gage dynamometer. The dynamometer was clamped on the machining center table. Reciprocating grinding was performed with a flat-faced diamond grinding wheel. Themore » output from the dynamometer was recorded with a data acquisition system and the normal component of the force was monitored. Experiments were carried out under various cutting conditions, different ELID conditions, and various grinding wheel bonds types. Rough grinding wheels of grit sizes {number_sign}170 and {number_sign}140 were used in the experiments. Compared to conventional grinding, there was a significant reduction in grinding force with ELID grinding. Therefore, ELID grinding can be recommended for high material removal rate grinding, low rigidity machines, and low rigidity workpieces. Compared to normal grinding, a reduction in grinding ratio was observed when ELID grinding was performed. A negative aspect of the process, this reduced G-ratio derives from bond erosion and can be improved somewhat by adjustments in the ELID current. The results of this investigation are discussed in detail in this report.« less

Development of a wearable measurement and control unit for personal customizing machine-supported exercise.

PubMed

Wang, Zhihui; Tamura, Naoki; Kiryu, Tohru

2005-01-01

Wearable technology has been used in various health-related fields to develop advanced monitoring solutions. However, the monitoring function alone cannot meet all the requirements of personal customizing machine-supported exercise that have biosignal-based controls. In this paper, we propose a new wearable unit design equipped with measurement and control functions to support the personal customization process. The wearable unit can measure the heart rate and electromyogram signals during exercise and output workload control commands to the exercise machines. We then applied a prototype of the wearable unit to an Internet-based cycle ergometer system. The wearable unit was examined using twelve young people to check its feasibility. The results verified that the unit could successfully adapt to the control of the workload and was effective for continuously supporting gradual changes in physical activities.

Monitoring of laser material processing using machine integrated low-coherence interferometry

NASA Astrophysics Data System (ADS)

Kunze, Rouwen; König, Niels; Schmitt, Robert

2017-06-01

Laser material processing has become an indispensable tool in modern production. With the availability of high power pico- and femtosecond laser sources, laser material processing is advancing into applications, which demand for highest accuracies such as laser micro milling or laser drilling. In order to enable narrow tolerance windows, a closedloop monitoring of the geometrical properties of the processed work piece is essential for achieving a robust manufacturing process. Low coherence interferometry (LCI) is a high-precision measuring principle well-known from surface metrology. In recent years, we demonstrated successful integrations of LCI into several different laser material processing methods. Within this paper, we give an overview about the different machine integration strategies, that always aim at a complete and ideally telecentric integration of the measurement device into the existing beam path of the processing laser. Thus, highly accurate depth measurements within machine coordinates and a subsequent process control and quality assurance are possible. First products using this principle have already found its way to the market, which underlines the potential of this technology for the monitoring of laser material processing.

An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.

PubMed

Wei, Ning; You, Jia; Friehs, Karl; Flaschel, Erwin; Nattkemper, Tim Wilhelm

2007-08-15

Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and viability during fermentation processes. For this purpose, an in situ probe has been developed, which utilizes a dark field illumination unit to obtain high contrast images with an integrated CCD camera. To test the probe, brewer's yeast Saccharomyces cerevisiae is chosen as the target microorganism. Images of the yeast cells in the bioreactors are captured, processed, and analyzed automatically by means of mechatronics, image processing, and machine learning. Two support vector machine based classifiers are used for separating cells from background, and for distinguishing live from dead cells afterwards. The evaluation of the in situ experiments showed strong correlation between results obtained by the probe and those by widely accepted standard methods. Thus, the in situ probe has been proved to be a feasible device for on-line monitoring of both cell density and viability with high accuracy and stability. (c) 2007 Wiley Periodicals, Inc.

Machinability of Stellite 6 hardfacing

NASA Astrophysics Data System (ADS)

Benghersallah, M.; Boulanouar, L.; Le Coz, G.; Devillez, A.; Dudzinski, D.

2010-06-01

This paper reports some experimental findings concerning the machinability at high cutting speed of nickel-base weld-deposited hardfacings for the manufacture of hot tooling. The forging work involves extreme impacts, forces, stresses and temperatures. Thus, mould dies must be extremely resistant. The aim of the project is to create a rapid prototyping process answering to forging conditions integrating a Stellite 6 hardfacing deposed PTA process. This study talks about the dry machining of the hardfacing, using a two tips machining tool and a high speed milling machine equipped by a power consumption recorder Wattpilote. The aim is to show the machinability of the hardfacing, measuring the power and the tip wear by optical microscope and white light interferometer, using different strategies and cutting conditions.

Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

PubMed

Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

2018-01-10

Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

Ant-Based Cyber Defense (also known as

DOE Office of Scientific and Technical Information (OSTI.GOV)

Glenn Fink, PNNL

2015-09-29

ABCD is a four-level hierarchy with human supervisors at the top, a top-level agent called a Sergeant controlling each enclave, Sentinel agents located at each monitored host, and mobile Sensor agents that swarm through the enclaves to detect cyber malice and misconfigurations. The code comprises four parts: (1) the core agent framework, (2) the user interface and visualization, (3) test-range software to create a network of virtual machines including a simulated Internet and user and host activity emulation scripts, and (4) a test harness to allow the safe running of adversarial code within the framework of monitored virtual machines.

A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

PubMed

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2015-12-01

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

Harvesting small trees for bio-energy

Treesearch

John Klepac; Robert Rummer; Jason Thompson

2011-01-01

A conventional whole-tree logging operation consisting of 4-wheeled and 3-wheeled saw-head feller-bunchers, two grapple skidders and a chipper that produces dirty chips was monitored across several stands and machine performance evaluated. Stands were inventoried to determine density, volume, and basal area per acre and will be used to relate machine performance to...

Transmission of hepatitis C virus between hemodialysis patients sharing the same machine.

PubMed

Sartor, Catherine; Brunet, Philippe; Simon, Sophie; Tamalet, Catherine; Berland, Yvon; Drancourt, Michel

2004-07-01

After a patient acquired hepatitis C virus (HCV) infection in our unit, we performed epidemiologic and virologic investigations, including genotyping and phylogenetic analyses. The results provided evidence for HCV transmission between two patients sharing the same machine and suggested possible transmission via accidental contamination of the venous pressure monitoring system.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

NASA Astrophysics Data System (ADS)

Tesoriero, Anthony J.; Gronberg, Jo Ann; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-08-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

Waveform classification and statistical analysis of seismic precursors to the July 2008 Vulcanian Eruption of Soufrière Hills Volcano, Montserrat

NASA Astrophysics Data System (ADS)

Rodgers, Mel; Smith, Patrick; Pyle, David; Mather, Tamsin

2016-04-01

Understanding the transition between quiescence and eruption at dome-forming volcanoes, such as Soufrière Hills Volcano (SHV), Montserrat, is important for monitoring volcanic activity during long-lived eruptions. Statistical analysis of seismic events (e.g. spectral analysis and identification of multiplets via cross-correlation) can be useful for characterising seismicity patterns and can be a powerful tool for analysing temporal changes in behaviour. Waveform classification is crucial for volcano monitoring, but consistent classification, both during real-time analysis and for retrospective analysis of previous volcanic activity, remains a challenge. Automated classification allows consistent re-classification of events. We present a machine learning (random forest) approach to rapidly classify waveforms that requires minimal training data. We analyse the seismic precursors to the July 2008 Vulcanian explosion at SHV and show systematic changes in frequency content and multiplet behaviour that had not previously been recognised. These precursory patterns of seismicity may be interpreted as changes in pressure conditions within the conduit during magma ascent and could be linked to magma flow rates. Frequency analysis of the different waveform classes supports the growing consensus that LP and Hybrid events should be considered end members of a continuum of low-frequency source processes. By using both supervised and unsupervised machine-learning methods we investigate the nature of waveform classification and assess current classification schemes.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

USGS Publications Warehouse

Tesoriero, Anthony J.; Gronberg, Jo Ann M.; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-01-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

A Machine Vision Quality Control System for Industrial Acrylic Fibre Production

NASA Astrophysics Data System (ADS)

Heleno, Paulo; Davies, Roger; Correia, Bento A. Brázio; Dinis, João

2002-12-01

This paper describes the implementation of INFIBRA, a machine vision system used in the quality control of acrylic fibre production. The system was developed by INETI under a contract with a leading industrial manufacturer of acrylic fibres. It monitors several parameters of the acrylic production process. This paper presents, after a brief overview of the system, a detailed description of the machine vision algorithms developed to perform the inspection tasks unique to this system. Some of the results of online operation are also presented.

Advances in Machine Technology.

PubMed

Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

2018-01-01

Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

Comparison of Automated and Manual Recording of Brief Episodes of Intracranial Hypertension and Cerebral Hypoperfusion and Their Association with Outcome After Severe Traumatic Brain Injury

DTIC Science & Technology

2017-03-01

neuro ICP care beyond trauma care. 15. SUBJECT TERMS Advanced machine learning techniques, intracranial pressure, vital signs, monitoring...death and disability in combat casualties [1,2]. Approximately 2 million head injuries occur annually in the United States, resulting in more than...editor. Machine learning and data mining in pattern recognition. Proceedings of the 8th International Workshop on Machine Learning and Data Mining in

The ultrastructural characteristics of porcine hepatocytes donated after cardiac death and preserved with warm machine perfusion preservation.

PubMed

Bochimoto, Hiroki; Matsuno, Naoto; Ishihara, Yo; Shonaka, Tatsuya; Koga, Daisuke; Hira, Yoshiki; Nishikawa, Yuji; Furukawa, Hiroyuki; Watanabe, Tsuyoshi

2017-01-01

The effects of warm machine perfusion preservation of liver grafts donated after cardiac death on the intracellular three-dimensional ultrastructure of the organelles in hepatocytes remain unclear. Here we analyzed comparatively the ultrastructure of the endomembrane systems in porcine hepatocytes under warm ischemia and successive hypothermic and midthermic machine perfusion preservation, a type of the warm machine perfusion. Porcine liver grafts which had a warm ischemia time of 60 minutes were perfused for 4 hours with modified University of Wisconsin gluconate solution. Group A grafts were preserved with hypothermic machine perfusion preservation at 8°C constantly for 4 hours. Group B grafts were preserved with rewarming up to 22°C by warm machine perfusion preservation for 4 hours. An analysis of hepatocytes after 60 minutes of warm ischemia by scanning electron microscope revealed the appearance of abnormal vacuoles and invagination of mitochondria. In the hepatocytes preserved by subsequent hypothermic machine perfusion preservation, strongly swollen mitochondria were observed. In contrast, the warm machine perfusion preservation could preserve the functional appearance of mitochondria in hepatocytes. Furthermore, abundant vacuoles and membranous structures sequestrating cellular organelles like autophagic vacuoles were frequently observed in hepatocytes after warm machine perfusion preservation. In conclusion, the ultrastructure of the endomembrane systems in the hepatocytes of liver grafts changed in accordance with the temperature conditions of machine perfusion preservation. In addition, temperature condition of the machine perfusion preservation may also affect the condition of the hepatic graft attributed to autophagy systems, and consequently alleviate the damage of the hepatocytes.

Comparison of shear wave velocities on ultrasound elastography between different machines, transducers, and acquisition depths: a phantom study.

PubMed

Shin, Hyun Joo; Kim, Myung-Joon; Kim, Ha Yan; Roh, Yun Ho; Lee, Mi-Jung

2016-10-01

To investigate consistency in shear wave velocities (SWVs) on ultrasound elastography using different machines, transducers and acquisition depths. The SWVs were measured using an elasticity phantom with a Young's modulus of 16.9 kPa, with three recently introduced ultrasound elastography machines (A, B and C from different vendors) and two transducers (low and high frequencies) at four depths (2, 3, 4 and 5 cm). Mean SWVs from 15 measurements and coefficient of variations (CVs) were compared between three machines, two transducers and four acquisition depths. The SWVs using the high frequency transducer were not acquired at 5 cm depth in machine B, and a high frequency transducer was not available in machine C. The mean SWVs in the three machines were different (p ≤ 0.002). The CVs were 0-0.09 in three machines. The mean SWVs between the two transducers were different (p < 0.001) except at 4 and 5 cm depths in machine A. The SWVs were affected by the acquisition depths in all conditions (p < 0.001). There is considerable difference in SWVs on ultrasound elastography depending on different machines, transducers and acquisition depths. Caution is needed when using the cutoff values of SWVs in different conditions. • The shear wave velocities (SWVs) are different between different ultrasound elastography machines • The SWVs are also different between different transducers and acquisition depths • Caution is needed when using the cutoff SWVs measured under different conditions.

On-line monitoring system of PV array based on internet of things technology

NASA Astrophysics Data System (ADS)

Li, Y. F.; Lin, P. J.; Zhou, H. F.; Chen, Z. C.; Wu, L. J.; Cheng, S. Y.; Su, F. P.

2017-11-01

The Internet of Things (IoT) Technology is used to inspect photovoltaic (PV) array which can greatly improve the monitoring, performance and maintenance of the PV array. In order to efficiently realize the remote monitoring of PV operating environment, an on-line monitoring system of PV array based on IoT is designed in this paper. The system includes data acquisition, data gateway and PV monitoring centre (PVMC) website. Firstly, the DSP-TMS320F28335 is applied to collect indicators of PV array using sensors, then the data are transmitted to data gateway through ZigBee network. Secondly, the data gateway receives the data from data acquisition part, obtains geographic information via GPS module, and captures the scenes around PV array via USB camera, then uploads them to PVMC website. Finally, the PVMC website based on Laravel framework receives all data from data gateway and displays them with abundant charts. Moreover, a fault diagnosis approach for PV array based on Extreme Learning Machine (ELM) is applied in PVMC. Once fault occurs, a user alert can be sent via E-mail. The designed system enables users to browse the operating conditions of PV array on PVMC website, including electrical, environmental parameters and video. Experimental results show that the presented monitoring system can efficiently real-time monitor the PV array, and the fault diagnosis approach reaches a high accuracy of 97.5%.

Risk estimation using probability machines.

PubMed

Dasgupta, Abhijit; Szymczak, Silke; Moore, Jason H; Bailey-Wilson, Joan E; Malley, James D

2014-03-01

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.

Effects of promotional materials on vending sales of low-fat items in teachers' lounges.

PubMed

Fiske, Amy; Cullen, Karen Weber

2004-01-01

This study examined the impact of an environmental intervention in the form of promotional materials and increased availability of low-fat items on vending machine sales. Ten vending machines were selected and randomly assigned to one of three conditions: control, or one of two experimental conditions. Vending machines in the two intervention conditions received three additional low-fat selections. Low-fat items were promoted at two levels: labels (intervention I), and labels plus signs (intervention II). The number of individual items sold and the total revenue generated was recorded weekly for each machine for 4 weeks. Use of promotional materials resulted in a small, but not significant, increase in the number of low-fat items sold, although machine sales were not significantly impacted by the change in product selection. Results of this study, although not statistically significant, suggest that environmental change may be a realistic means of positively influencing consumer behavior.

Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology.

PubMed

Oliver, Ruth Y; Ellis, Daniel P W; Chmura, Helen E; Krause, Jesse S; Pérez, Jonathan H; Sweet, Shannan K; Gough, Laura; Wingfield, John C; Boelman, Natalie T

2018-06-01

Bioacoustic networks could vastly expand the coverage of wildlife monitoring to complement satellite observations of climate and vegetation. This approach would enable global-scale understanding of how climate change influences phenomena such as migratory timing of avian species. The enormous data sets that autonomous recorders typically generate demand automated analyses that remain largely undeveloped. We devised automated signal processing and machine learning approaches to estimate dates on which songbird communities arrived at arctic breeding grounds. Acoustically estimated dates agreed well with those determined via traditional surveys and were strongly related to the landscape's snow-free dates. We found that environmental conditions heavily influenced daily variation in songbird vocal activity, especially before egg laying. Our novel approaches demonstrate that variation in avian migratory arrival can be detected autonomously. Large-scale deployment of this innovation in wildlife monitoring would enable the coverage necessary to assess and forecast changes in bird migration in the face of climate change.

Some problems of control of dynamical conditions of technological vibrating machines

NASA Astrophysics Data System (ADS)

Kuznetsov, N. K.; Lapshin, V. L.; Eliseev, A. V.

2017-10-01

The possibility of control of dynamical condition of the shakers that are designed for vibration treatment of parts interacting with granular media is discussed. The aim of this article is to develop the methodological basis of technology of creation of mathematical models of shake tables and the development of principles of formation of vibrational fields, estimation of their parameters and control of the structure vibration fields. Approaches to build mathematical models that take into account unilateral constraints, the relationships between elements, with the vibrating surface are developed. Methods intended to construct mathematical model of linear mechanical oscillation systems are used. Small oscillations about the position of static equilibrium are performed. The original method of correction of vibration fields by introduction of the oscillating system additional ties to the structure are proposed. Additional ties are implemented in the form of a mass-inertial device for changing the inertial parameters of the working body of the vibration table by moving the mass-inertial elements. The concept of monitoring the dynamic state of the vibration table based on the original measuring devices is proposed. Estimation for possible changes in dynamic properties is produced. The article is of interest for specialists in the field of creation of vibration technology machines and equipment.

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

The influence of machining condition and cutting tool wear on surface roughness of AISI 4340 steel

NASA Astrophysics Data System (ADS)

Natasha, A. R.; Ghani, J. A.; Che Haron, C. H.; Syarif, J.

2018-01-01

Sustainable machining by using cryogenic coolant as the cutting fluid has been proven to enhance some machining outputs. The main objective of the current work was to investigate the influence of machining conditions; dry and cryogenic, as well as the cutting tool wear on the machined surface roughness of AISI 4340 steel. The experimental tests were performed using chemical vapor deposition (CVD) coated carbide inserts. The value of machined surface roughness were measured at 3 cutting intervals; beginning, middle, and end of the cutting based on the readings of the tool flank wear. The results revealed that cryogenic turning had the greatest influence on surface roughness when machined at lower cutting speed and higher feed rate. Meanwhile, the cutting tool wear was also found to influence the surface roughness, either improving it or deteriorating it, based on the severity and the mechanism of the flank wear.

Design of a real-time tax-data monitoring intelligent card system

NASA Astrophysics Data System (ADS)

Gu, Yajun; Bi, Guotang; Chen, Liwei; Wang, Zhiyuan

2009-07-01

To solve the current problem of low efficiency of domestic Oil Station's information management, Oil Station's realtime tax data monitoring system has been developed to automatically access tax data of Oil pumping machines, realizing Oil-pumping machines' real-time automatic data collection, displaying and saving. The monitoring system uses the noncontact intelligent card or network to directly collect data which can not be artificially modified and so seals the loopholes and improves the tax collection's automatic level. It can perform real-time collection and management of the Oil Station information, and find the problem promptly, achieves the automatic management for the entire process covering Oil sales accounting and reporting. It can also perform remote query to the Oil Station's operation data. This system has broad application future and economic value.

Feature and Statistical Model Development in Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Kim, Inho

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns. Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

Predictive modeling for corrective maintenance of imaging devices from machine logs.

PubMed

Patil, Ravindra B; Patil, Meru A; Ravi, Vidya; Naik, Sarif

2017-07-01

In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers (OEMs). In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it. We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. One of the use-case of predicting component failure of PHILIPS iXR system is explained in this article.

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

PubMed

Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

2016-07-01

The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

Application of Machine Learning to Rotorcraft Health Monitoring

NASA Technical Reports Server (NTRS)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

A PLL-based resampling technique for vibration analysis in variable-speed wind turbines with PMSG: A bearing fault case

NASA Astrophysics Data System (ADS)

Pezzani, Carlos M.; Bossio, José M.; Castellino, Ariel M.; Bossio, Guillermo R.; De Angelo, Cristian H.

2017-02-01

Condition monitoring in permanent magnet synchronous machines has gained interest due to the increasing use in applications such as electric traction and power generation. Particularly in wind power generation, non-invasive condition monitoring techniques are of great importance. Usually, in such applications the access to the generator is complex and costly, while unexpected breakdowns results in high repair costs. This paper presents a technique which allows using vibration analysis for bearing fault detection in permanent magnet synchronous generators used in wind turbines. Given that in wind power applications the generator rotational speed may vary during normal operation, it is necessary to use special sampling techniques to apply spectral analysis of mechanical vibrations. In this work, a resampling technique based on order tracking without measuring the rotor position is proposed. To synchronize sampling with rotor position, an estimation of the rotor position obtained from the angle of the voltage vector is proposed. This angle is obtained from a phase-locked loop synchronized with the generator voltages. The proposed strategy is validated by laboratory experimental results obtained from a permanent magnet synchronous generator. Results with single point defects in the outer race of a bearing under variable speed and load conditions are presented.

In-process, non-destructive multimodal dynamic testing of high-speed composite rotors

NASA Astrophysics Data System (ADS)

Kuschmierz, Robert; Filippatos, Angelos; Langkamp, Albert; Hufenbach, Werner; Czarske, Jürgern W.; Fischer, Andreas

2014-03-01

Fibre reinforced plastic (FRP) rotors are lightweight and offer great perspectives in high-speed applications such as turbo machinery. Currently, novel rotor structures and materials are investigated for the purpose of increasing machine efficiency, lifetime and loading limits. Due to complex rotor structures, high anisotropy and non-linear behavior of FRP under dynamic loads, an in-process measurement system is necessary to monitor and to investigate the evolution of damages under real operation conditions. A non-invasive, optical laser Doppler distance sensor measurement system is applied to determine the biaxial deformation of a bladed FRP rotor with micron uncertainty as well as the tangential blade vibrations at surface speeds above 300 m/s. The laser Doppler distance sensor is applicable under vacuum conditions. Measurements at varying loading conditions are used to determine elastic and plastic deformations. Furthermore they allow to determine hysteresis, fatigue, Eigenfrequency shifts and loading limits. The deformation measurements show a highly anisotropic and nonlinear behavior and offer a deeper understanding of the damage evolution in FRP rotors. The experimental results are used to validate and to calibrate a simulation model of the deformation. The simulation combines finite element analysis and a damage mechanics model. The combination of simulation and measurement system enables the monitoring and prediction of damage evolutions of FRP rotors in process.

Machine Learning Assessments of Soil Drying

NASA Astrophysics Data System (ADS)

Coopersmith, E. J.; Minsker, B. S.; Wenzel, C.; Gilmore, B. J.

2011-12-01

Agricultural activities require the use of heavy equipment and vehicles on unpaved farmlands. When soil conditions are wet, equipment can cause substantial damage, leaving deep ruts. In extreme cases, implements can sink and become mired, causing considerable delays and expense to extricate the equipment. Farm managers, who are often located remotely, cannot assess sites before allocating equipment, causing considerable difficulty in reliably assessing conditions of countless sites with any reliability and frequency. For example, farmers often trace serpentine paths of over one hundred miles each day to assess the overall status of various tracts of land spanning thirty, forty, or fifty miles in each direction. One means of assessing the moisture content of a field lies in the strategic positioning of remotely-monitored in situ sensors. Unfortunately, land owners are often reluctant to place sensors across their properties due to the significant monetary cost and complexity. This work aspires to overcome these limitations by modeling the process of wetting and drying statistically - remotely assessing field readiness using only information that is publically accessible. Such data includes Nexrad radar and state climate network sensors, as well as Twitter-based reports of field conditions for validation. Three algorithms, classification trees, k-nearest-neighbors, and boosted perceptrons are deployed to deliver statistical field readiness assessments of an agricultural site located in Urbana, IL. Two of the three algorithms performed with 92-94% accuracy, with the majority of misclassifications falling within the calculated margins of error. This demonstrates the feasibility of using a machine learning framework with only public data, knowledge of system memory from previous conditions, and statistical tools to assess "readiness" without the need for real-time, on-site physical observation. Future efforts will produce a workflow assimilating Nexrad, climate network, and Twitter data to generate a real-time web-map of estimated readiness conditions.

An In-Process Surface Roughness Recognition System in End Milling Operations

ERIC Educational Resources Information Center

Yang, Lieh-Dai; Chen, Joseph C.

2004-01-01

To develop an in-process quality control system, a sensor technique and a decision-making algorithm need to be applied during machining operations. Several sensor techniques have been used in the in-process prediction of quality characteristics in machining operations. For example, an accelerometer sensor can be used to monitor the vibration of…

Elevating Virtual Machine Introspection for Fine-Grained Process Monitoring: Techniques and Applications

ERIC Educational Resources Information Center

Srinivasan, Deepa

2013-01-01

Recent rapid malware growth has exposed the limitations of traditional in-host malware-defense systems and motivated the development of secure virtualization-based solutions. By running vulnerable systems as virtual machines (VMs) and moving security software from inside VMs to the outside, the out-of-VM solutions securely isolate the anti-malware…

Machine learning techniques for fault isolation and sensor placement

NASA Technical Reports Server (NTRS)

Carnes, James R.; Fisher, Douglas H.

1993-01-01

Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.

Development of a Tool Condition Monitoring System for Impregnated Diamond Bits in Rock Drilling Applications

NASA Astrophysics Data System (ADS)

Perez, Santiago; Karakus, Murat; Pellet, Frederic

2017-05-01

The great success and widespread use of impregnated diamond (ID) bits are due to their self-sharpening mechanism, which consists of a constant renewal of diamonds acting at the cutting face as the bit wears out. It is therefore important to keep this mechanism acting throughout the lifespan of the bit. Nonetheless, such a mechanism can be altered by the blunting of the bit that ultimately leads to a less than optimal drilling performance. For this reason, this paper aims at investigating the applicability of artificial intelligence-based techniques in order to monitor tool condition of ID bits, i.e. sharp or blunt, under laboratory conditions. Accordingly, topologically invariant tests are carried out with sharp and blunt bits conditions while recording acoustic emissions (AE) and measuring-while-drilling variables. The combined output of acoustic emission root-mean-square value (AErms), depth of cut ( d), torque (tob) and weight-on-bit (wob) is then utilized to create two approaches in order to predict the wear state condition of the bits. One approach is based on the combination of the aforementioned variables and another on the specific energy of drilling. The two different approaches are assessed for classification performance with various pattern recognition algorithms, such as simple trees, support vector machines, k-nearest neighbour, boosted trees and artificial neural networks. In general, Acceptable pattern recognition rates were obtained, although the subset composed by AErms and tob excels due to the high classification performances rates and fewer input variables.

A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

NASA Astrophysics Data System (ADS)

Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

2017-12-01

Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

40 CFR Appendix D to Subpart A of... - Harmonized Tariff Schedule Description of Products That May Contain Controlled Substances in...

Code of Federal Regulations, 2014 CFR

2014-07-01

... Conditioning/Heat Pump Equipment Domestic and commercial air conditioning and refrigeration equipment fall... cooling/heat cycle. 8415.82.00 Other, incorporating a refrigerating unit— Self-contained machines and... refrigerating or freezing equipment, electric or other; heat pumps, other than air conditioning machines of...

40 CFR Appendix D to Subpart A of... - Harmonized Tariff Schedule Description of Products That May Contain Controlled Substances in...

Code of Federal Regulations, 2011 CFR

2011-07-01

... Conditioning/Heat Pump Equipment Domestic and commercial air conditioning and refrigeration equipment fall... cooling/heat cycle. 8415.82.00 Other, incorporating a refrigerating unit— Self-contained machines and... refrigerating or freezing equipment, electric or other; heat pumps, other than air conditioning machines of...

40 CFR Appendix D to Subpart A of... - Harmonized Tariff Schedule Description of Products That May Contain Controlled Substances in...

Code of Federal Regulations, 2010 CFR

2010-07-01

... Conditioning/Heat Pump Equipment Domestic and commercial air conditioning and refrigeration equipment fall... cooling/heat cycle. 8415.82.00 Other, incorporating a refrigerating unit— Self-contained machines and... refrigerating or freezing equipment, electric or other; heat pumps, other than air conditioning machines of...

40 CFR Appendix D to Subpart A of... - Harmonized Tariff Schedule Description of Products That May Contain Controlled Substances in...

Code of Federal Regulations, 2013 CFR

2013-07-01

... Conditioning/Heat Pump Equipment Domestic and commercial air conditioning and refrigeration equipment fall... cooling/heat cycle. 8415.82.00 Other, incorporating a refrigerating unit— Self-contained machines and... refrigerating or freezing equipment, electric or other; heat pumps, other than air conditioning machines of...

40 CFR Appendix D to Subpart A of... - Harmonized Tariff Schedule Description of Products That May Contain Controlled Substances in...

Code of Federal Regulations, 2012 CFR

2012-07-01

... Conditioning/Heat Pump Equipment Domestic and commercial air conditioning and refrigeration equipment fall... cooling/heat cycle. 8415.82.00 Other, incorporating a refrigerating unit— Self-contained machines and... refrigerating or freezing equipment, electric or other; heat pumps, other than air conditioning machines of...

How Will I Be Monitored After Heart Surgery?

MedlinePlus

... monitor you are described below. What is an ECG? •An electrocardiogram, or ECG or EKG machine, records your heartbeat. • Tiny wires, ... normally. •A highly trained nurse will watch the ECG at all times. •You’ll be hooked up ...

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Mechanical properties of a new mica-based machinable glass ceramic for CAD/CAM restorations.

PubMed

Thompson, J Y; Bayne, S C; Heymann, H O

1996-12-01

Machinable ceramics (Vita Mark II and Dicor MGC) exhibit good short-term clinical performance, but long-term in vivo fracture resistance is still being monitored. The relatively low fracture toughness of currently available machinable ceramics restricts their use to conservative inlays and onlays. A new machinable glass ceramic (MGC-F) has been developed (Corning Inc.) with enhanced fluorescence and machinability. The purpose of this study was to characterize and compare key mechanical properties of MGC-F to Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II glass ceramics. The mean fracture toughness and indented biaxial flexure strength of MGC-F were each significantly greater (p < or = 0.01) than that of Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II ceramic materials. The results of this study indicate the potential for better in vivo fracture resistance of MGC-F compared with existing machinable ceramic materials for CAD/CAM restorations.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Acoustical Detection Of Leakage In A Combustor

NASA Technical Reports Server (NTRS)

Puster, Richard L.; Petty, Jeffrey L.

1993-01-01

Abnormal combustion excites characteristic standing wave. Acoustical leak-detection system gives early warning of failure, enabling operating personnel to stop combustion process and repair spray bar before leak grows large enough to cause damage. Applicable to engines, gas turbines, furnaces, and other machines in which acoustic emissions at known frequencies signify onset of damage. Bearings in rotating machines monitored for emergence of characteristic frequencies shown in previous tests associated with incipient failure. Also possible to monitor for signs of trouble at multiple frequencies by feeding output of transducer simultaneously to multiple band-pass filters and associated circuitry, including separate trigger circuit set to appropriate level for each frequency.

Flexible architecture of data acquisition firmware based on multi-behaviors finite state machine

NASA Astrophysics Data System (ADS)

Arpaia, Pasquale; Cimmino, Pasquale

2016-11-01

A flexible firmware architecture for different kinds of data acquisition systems, ranging from high-precision bench instruments to low-cost wireless transducers networks, is presented. The key component is a multi-behaviors finite state machine, easily configurable to both low- and high-performance requirements, to diverse operating systems, as well as to on-line and batch measurement algorithms. The proposed solution was validated experimentally on three case studies with data acquisition architectures: (i) concentrated, in a high-precision instrument for magnetic measurements at CERN, (ii) decentralized, for telemedicine remote monitoring of patients at home, and (iii) distributed, for remote monitoring of building's energy loss.

Acoustic emission from single point machining: Part 2, Signal changes with tool wear

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-01-01

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

A testing machine for dental air-turbine handpiece characteristics: free-running speed, stall torque, bearing resistance.

PubMed

Darvell, Brain W; Dyson, J E

2005-01-01

The measurement of performance characteristics of dental air turbine handpieces is of interest with respect to product comparisons, standards specifications and monitoring of bearing longevity in clinical service. Previously, however, bulky and expensive laboratory equipment was required. A portable test machine is described for determining three key characteristics of dental air-turbine handpieces: free-running speed, stall torque and bearing resistance. It relies on a special circuit design for performing a hardware integration of a force signal with respect to rotational position, independent of the rate at which the turbine is allowed to turn during both stall torque and bearing resistance measurements. Free-running speed without the introduction of any imbalance can be readily monitored. From the essential linear relationship between torque and speed, dynamic torque and, hence, power, can then be calculated. In order for these measurements to be performed routinely with the necessary precision of location on the test stage, a detailed procedure for ensuring proper gripping of the handpiece is described. The machine may be used to verify performance claims, standard compliance checks should this be established as appropriate, monitor deterioration with time and usage in the clinical environment and for laboratory investigation of design development.

Linear positioning laser calibration setup of CNC machine tools

NASA Astrophysics Data System (ADS)

Sui, Xiulin; Yang, Congjing

2002-10-01

The linear positioning laser calibration setup of CNC machine tools is capable of executing machine tool laser calibraiotn and backlash compensation. Using this setup, hole locations on CNC machien tools will be correct and machien tool geometry will be evaluated and adjusted. Machien tool laser calibration and backlash compensation is a simple and straightforward process. First the setup is to 'find' the stroke limits of the axis. Then the laser head is then brought into correct alignment. Second is to move the machine axis to the other extreme, the laser head is now aligned, using rotation and elevation adjustments. Finally the machine is moved to the start position and final alignment is verified. The stroke of the machine, and the machine compensation interval dictate the amount of data required for each axis. These factors determine the amount of time required for a through compensation of the linear positioning accuracy. The Laser Calibrator System monitors the material temperature and the air density; this takes into consideration machine thermal growth and laser beam frequency. This linear positioning laser calibration setup can be used on CNC machine tools, CNC lathes, horizontal centers and vertical machining centers.

Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring

PubMed Central

Peng, Yeping; Wu, Tonghai; Wang, Shuo; Kwok, Ngaiming; Peng, Zhongxiao

2015-01-01

On-line images of wear debris contain important information for real-time condition monitoring, and a dynamic imaging technique can eliminate particle overlaps commonly found in static images, for instance, acquired using ferrography. However, dynamic wear debris images captured in a running machine are unavoidably blurred because the particles in lubricant are in motion. Hence, it is difficult to acquire reliable images of wear debris with an adequate resolution for particle feature extraction. In order to obtain sharp wear particle images, an image processing approach is proposed. Blurred particles were firstly separated from the static background by utilizing a background subtraction method. Second, the point spread function was estimated using power cepstrum to determine the blur direction and length. Then, the Wiener filter algorithm was adopted to perform image restoration to improve the image quality. Finally, experiments were conducted with a large number of dynamic particle images to validate the effectiveness of the proposed method and the performance of the approach was also evaluated. This study provides a new practical approach to acquire clear images for on-line wear monitoring. PMID:25856328

Intelligent image processing for machine safety

NASA Astrophysics Data System (ADS)

Harvey, Dennis N.

1994-10-01

This paper describes the use of intelligent image processing as a machine guarding technology. One or more color, linear array cameras are positioned to view the critical region(s) around a machine tool or other piece of manufacturing equipment. The image data is processed to provide indicators of conditions dangerous to the equipment via color content, shape content, and motion content. The data from these analyses is then sent to a threat evaluator. The purpose of the evaluator is to determine if a potentially machine-damaging condition exists based on the analyses of color, shape, and motion, and on `knowledge' of the specific environment of the machine. The threat evaluator employs fuzzy logic as a means of dealing with uncertainty in the vision data.

Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.

PubMed

Leal, Yenny; Gonzalez-Abril, Luis; Lorencio, Carol; Bondia, Jorge; Vehi, Josep

2013-07-01

Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

Monitoring and decision making by people in man machine systems

NASA Technical Reports Server (NTRS)

Johannsen, G.

1979-01-01

The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.

Aspect-Oriented Monitoring of C Programs

NASA Technical Reports Server (NTRS)

Havelund, Klaus; VanWyk, Eric

2008-01-01

The paper presents current work on extending ASPECTC with state machines, resulting in a framework for aspect-oriented monitoring of C programs. Such a framework can be used for testing purposes, or it can be part of a fault protection strategy. The long term goal is to explore the synergy between the fields of runtime verification, focused on program monitoring, and aspect-oriented programming, focused on more general program development issues. The work is inspired by the observation that most work in this direction has been done for JAVA, partly due to the lack of easily accessible extensible compiler frameworks for C. The work is performed using the SILVER extensible attribute grammar compiler framework, in which C has been defined as a host language. Our work consists of extending C with ASPECTC, and subsequently to extend ASPECTC with state machines.

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes.

PubMed

Ling, Sai Ho; San, Phyo Phyo; Nguyen, Hung T

2016-09-01

Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Machine for use in monitoring fatigue life for a plurality of elastomeric specimens

NASA Technical Reports Server (NTRS)

Fitzer, G. E. (Inventor)

1977-01-01

An improved machine is described for use in determining the fatigue life for elastomeric specimens. The machine is characterized by a plurality of juxtaposed test stations, specimen support means located at each of the test stations for supporting a plurality of specimens of elastomeric material, and means for subjecting the specimens at each of said stations to sinusoidal strain at a strain rate unique with respect to the strain rate at which the specimens at each of the other stations is subjected to sinusoidal strain.

[Evaluation of Medical Instruments Cleaning Effect of Fluorescence Detection Technique].

PubMed

Sheng, Nan; Shen, Yue; Li, Zhen; Li, Huijuan; Zhou, Chaoqun

2016-01-01

To compare the cleaning effect of automatic cleaning machine and manual cleaning on coupling type surgical instruments. A total of 32 cleaned medical instruments were randomly sampled from medical institutions in Putuo District medical institutions disinfection supply center. Hygiena System SUREII ATP was used to monitor the ATP value, and the cleaning effect was evaluated. The surface ATP values of the medical instrument of manual cleaning were higher than that of the automatic cleaning machine. Coupling type surgical instruments has better cleaning effect of automatic cleaning machine before disinfection, the application is recommended.

Environmental noise forecasting based on support vector machine

NASA Astrophysics Data System (ADS)

Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

2018-01-01

As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

PubMed

Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang

2012-01-01

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.

Effective Dust Control Systems on Concrete Dowel Drilling Machinery

PubMed Central

Echt, Alan S.; Sanderson, Wayne T.; Mead, Kenneth R.; Feng, H. Amy; Farwick, Daniel R.; Farwick, Dawn Ramsey

2016-01-01

Rotary-type percussion dowel drilling machines, which drill horizontal holes in concrete pavement, have been documented to produce respirable crystalline silica concentrations above recommended exposure criteria. This places operators at potential risk for developing health effects from exposure. United States manufacturers of these machines offer optional dust control systems. The effectiveness of the dust control systems to reduce respirable dust concentrations on two types of drilling machines was evaluated under controlled conditions with the machines operating inside large tent structures in an effort to eliminate secondary exposure sources not related to the dowel-drilling operation. Area air samples were collected at breathing zone height at three locations around each machine. Through equal numbers of sampling rounds with the control systems randomly selected to be on or off, the control systems were found to significantly reduce respirable dust concentrations from a geometric mean of 54 mg per cubic meter to 3.0 mg per cubic meter on one machine and 57 mg per cubic meter to 5.3 mg per cubic meter on the other machine. This research shows that the dust control systems can dramatically reduce respirable dust concentrations by over 90% under controlled conditions. However, these systems need to be evaluated under actual work conditions to determine their effectiveness in reducing worker exposures to crystalline silica below hazardous levels. PMID:27074062

The prediction of food additives in the fruit juice based on electronic nose with chemometrics.

PubMed

Qiu, Shanshan; Wang, Jun

2017-09-01

Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R 2 s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring. Copyright © 2017 Elsevier Ltd. All rights reserved.

Monitoring Business Activity

DTIC Science & Technology

2006-03-01

AFRL-IF-RS-TR-2006-88 Final Technical Report March 2006 MONITORING BUSINESS ACTIVITY New York University...REPORT DATE MARCH 2006 3. REPORT TYPE AND DATES COVERED Final Sep 01 – Oct 05 4. TITLE AND SUBTITLE MONITORING BUSINESS ACTIVITY 6. AUTHOR(S...Accepted to Journal of Machine Learning Research, pending revisions. CeDER Working Paper #CeDER-04-08, Stern School of Business , New York University

AC Loss Analysis of MgB2-Based Fully Superconducting Machines

NASA Astrophysics Data System (ADS)

Feddersen, M.; Haran, K. S.; Berg, F.

2017-12-01

Superconducting electric machines have shown potential for significant increase in power density, making them attractive for size and weight sensitive applications such as offshore wind generation, marine propulsion, and hybrid-electric aircraft propulsion. Superconductors exhibit no loss under dc conditions, though ac current and field produce considerable losses due to hysteresis, eddy currents, and coupling mechanisms. For this reason, many present machines are designed to be partially superconducting, meaning that the dc field components are superconducting while the ac armature coils are conventional conductors. Fully superconducting designs can provide increases in power density with significantly higher armature current; however, a good estimate of ac losses is required to determine the feasibility under the machines intended operating conditions. This paper aims to characterize the expected losses in a fully superconducting machine targeted towards aircraft, based on an actively-shielded, partially superconducting machine from prior work. Various factors are examined such as magnet strength, operating frequency, and machine load to produce a model for the loss in the superconducting components of the machine. This model is then used to optimize the design of the machine for minimal ac loss while maximizing power density. Important observations from the study are discussed.

Beam Loss Monitoring for LHC Machine Protection

NASA Astrophysics Data System (ADS)

Holzer, Eva Barbara; Dehning, Bernd; Effnger, Ewald; Emery, Jonathan; Grishin, Viatcheslav; Hajdu, Csaba; Jackson, Stephen; Kurfuerst, Christoph; Marsili, Aurelien; Misiowiec, Marek; Nagel, Markus; Busto, Eduardo Nebot Del; Nordt, Annika; Roderick, Chris; Sapinski, Mariusz; Zamantzas, Christos

The energy stored in the nominal LHC beams is two times 362 MJ, 100 times the energy of the Tevatron. As little as 1 mJ/cm3 deposited energy quenches a magnet at 7 TeV and 1 J/cm3 causes magnet damage. The beam dumps are the only places to safely dispose of this beam. One of the key systems for machine protection is the beam loss monitoring (BLM) system. About 3600 ionization chambers are installed at likely or critical loss locations around the LHC ring. The losses are integrated in 12 time intervals ranging from 40 μs to 84 s and compared to threshold values defined in 32 energy ranges. A beam abort is requested when potentially dangerous losses are detected or when any of the numerous internal system validation tests fails. In addition, loss data are used for machine set-up and operational verifications. The collimation system for example uses the loss data for set-up and regular performance verification. Commissioning and operational experience of the BLM are presented: The machine protection functionality of the BLM system has been fully reliable; the LHC availability has not been compromised by false beam aborts.

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

PubMed Central

Morison, Gordon; Boreham, Philip

2018-01-01

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. PMID:29385030

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Method of Individual Forecasting of Technical State of Logging Machines

NASA Astrophysics Data System (ADS)

Kozlov, V. G.; Gulevsky, V. A.; Skrypnikov, A. V.; Logoyda, V. S.; Menzhulova, A. S.

2018-03-01

Development of the model that evaluates the possibility of failure requires the knowledge of changes’ regularities of technical condition parameters of the machines in use. To study the regularities, the need to develop stochastic models that take into account physical essence of the processes of destruction of structural elements of the machines, the technology of their production, degradation and the stochastic properties of the parameters of the technical state and the conditions and modes of operation arose.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PubMed

Li, Baihua; Sharma, Arjun; Meng, James; Purushwalkam, Senthil; Gowen, Emma

2017-01-01

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

Drilling High Precision Holes in Ti6Al4V Using Rotary Ultrasonic Machining and Uncertainties Underlying Cutting Force, Tool Wear, and Production Inaccuracies.

PubMed

Chowdhury, M A K; Sharif Ullah, A M M; Anwar, Saqib

2017-09-12

Ti6Al4V alloys are difficult-to-cut materials that have extensive applications in the automotive and aerospace industry. A great deal of effort has been made to develop and improve the machining operations of Ti6Al4V alloys. This paper presents an experimental study that systematically analyzes the effects of the machining conditions (ultrasonic power, feed rate, spindle speed, and tool diameter) on the performance parameters (cutting force, tool wear, overcut error, and cylindricity error), while drilling high precision holes on the workpiece made of Ti6Al4V alloys using rotary ultrasonic machining (RUM). Numerical results were obtained by conducting experiments following the design of an experiment procedure. The effects of the machining conditions on each performance parameter have been determined by constructing a set of possibility distributions (i.e., trapezoidal fuzzy numbers) from the experimental data. A possibility distribution is a probability-distribution-neural representation of uncertainty, and is effective in quantifying the uncertainty underlying physical quantities when there is a limited number of data points which is the case here. Lastly, the optimal machining conditions have been identified using these possibility distributions.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

PubMed

Morawska, Lidia; Thai, Phong K; Liu, Xiaoting; Asumadu-Sakyi, Akwasi; Ayoko, Godwin; Bartonova, Alena; Bedini, Andrea; Chai, Fahe; Christensen, Bryce; Dunbabin, Matthew; Gao, Jian; Hagler, Gayle S W; Jayaratne, Rohan; Kumar, Prashant; Lau, Alexis K H; Louie, Peter K K; Mazaheri, Mandana; Ning, Zhi; Motta, Nunzio; Mullins, Ben; Rahman, Md Mahmudur; Ristovski, Zoran; Shafiei, Mahnaz; Tjondronegoro, Dian; Westerdahl, Dane; Williams, Ron

2018-07-01

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve. Copyright © 2018 Elsevier Ltd. All rights reserved.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

In vivo quantification of plant starch reserves at micrometer resolution using X-ray microCT imaging and machine learning.

PubMed

Earles, J Mason; Knipfer, Thorsten; Tixier, Aude; Orozco, Jessica; Reyes, Clarissa; Zwieniecki, Maciej A; Brodersen, Craig R; McElrone, Andrew J

2018-03-08

Starch is the primary energy storage molecule used by most terrestrial plants to fuel respiration and growth during periods of limited to no photosynthesis, and its depletion can drive plant mortality. Destructive techniques at coarse spatial scales exist to quantify starch, but these techniques face methodological challenges that can lead to uncertainty about the lability of tissue-specific starch pools and their role in plant survival. Here, we demonstrate how X-ray microcomputed tomography (microCT) and a machine learning algorithm can be coupled to quantify plant starch content in vivo, repeatedly and nondestructively over time in grapevine stems (Vitis spp.). Starch content estimated for xylem axial and ray parenchyma cells from microCT images was correlated strongly with enzymatically measured bulk-tissue starch concentration on the same stems. After validating our machine learning algorithm, we then characterized the spatial distribution of starch concentration in living stems at micrometer resolution, and identified starch depletion in live plants under experimental conditions designed to halt photosynthesis and starch production, initiating the drawdown of stored starch pools. Using X-ray microCT technology for in vivo starch monitoring should enable novel research directed at resolving the spatial and temporal patterns of starch accumulation and depletion in woody plant species. No claim to original US Government works New Phytologist © 2018 New Phytologist Trust.

Virtual sensors for on-line wheel wear and part roughness measurement in the grinding process.

PubMed

Arriandiaga, Ander; Portillo, Eva; Sánchez, Jose A; Cabanes, Itziar; Pombo, Iñigo

2014-05-19

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.

Analysis of surface integrity in machining of AISI 304 stainless steel under various cooling and cutting conditions

NASA Astrophysics Data System (ADS)

Klocke, F.; Döbbeler, B.; Lung, S.; Seelbach, T.; Jawahir, I. S.

2018-05-01

Recent studies have shown that machining under specific cooling and cutting conditions can be used to induce a nanocrystalline surface layer in the workspiece. This layer has beneficial properties, such as improved fatigue strength, wear resistance and tribological behavior. In machining, a promising approach for achieving grain refinement in the surface layer is the application of cryogenic cooling. The aim is to use the last step of the machining operation to induce the desired surface quality to save time-consuming and expensive post machining surface treatments. The material used in this study was AISI 304 stainless steel. This austenitic steel suffers from low yield strength that limits its technological applications. In this paper, liquid nitrogen (LN2) as cryogenic coolant, as well as minimum quantity lubrication (MQL), was applied and investigated. As a reference, conventional flood cooling was examined. Besides the cooling conditions, the feed rate was varied in four steps. A large rounded cutting edge radius and finishing cutting parameters were chosen to increase the mechanical load on the machined surface. The surface integrity was evaluated at both, the microstructural and the topographical levels. After turning experiments, a detailed analysis of the microstructure was carried out including the imaging of the surface layer and hardness measurements at varying depths within the machined layer. Along with microstructural investigations, different topological aspects, e.g., the surface roughness, were analyzed. It was shown that the resulting microstructure strongly depends on the cooling condition. This study also shows that it was possible to increase the micro hardness in the top surface layer significantly.

Comparative Study of Vibration Condition Indicators for Detecting Cracks in Spur Gears

NASA Technical Reports Server (NTRS)

Nanadic, Nenad; Ardis, Paul; Hood, Adrian; Thurston, Michael; Ghoshal, Anindya; Lewicki, David

2013-01-01

This paper reports the results of an empirical study on the tooth breakage failure mode in spur gears. Of four dominant gear failure modes (breakage, wear, pitting, and scoring), tooth breakage is the most precipitous and often leads to catastrophic failures. The cracks were initiated using a fatigue tester and a custom-designed single-tooth bending fixture to simulate over-load conditions, instead of traditional notching using wire electrical discharge machining (EDM). The cracks were then propagated on a dynamometer. The ground truth of damage level during crack propagation was monitored with crack-propagation sensors. Ten crack propagations have been performed to compare the existing condition indicators (CIs) with respect to their: ability to detect a crack, ability to assess the damage, and sensitivity to sensor placement. Of more than thirty computed CIs, this paper compares five commonly used: raw RMS, FM0, NA4, raw kurtosis, and NP4. The performance of combined CIs was also investigated, using linear, logistic, and boosted regression trees based feature fusion.

[A new machinability test machine and the machinability of composite resins for core built-up].

PubMed

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

A Machine LearningFramework to Forecast Wave Conditions

NASA Astrophysics Data System (ADS)

Zhang, Y.; James, S. C.; O'Donncha, F.

2017-12-01

Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in computational expense. The low computational cost (and by association low computer-power requirement) means that the machine learning algorithms could be installed on a wave-energy converter as a form of "edge computing" where a device could forecast its own 48-hour energy production.

Effects of Selected Task Performance Criteria at Initiating Adaptive Task Real locations

NASA Technical Reports Server (NTRS)

Montgomery, Demaris A.

2001-01-01

In the current report various performance assessment methods used to initiate mode transfers between manual control and automation for adaptive task reallocation were tested. Participants monitored two secondary tasks for critical events while actively controlling a process in a fictional system. One of the secondary monitoring tasks could be automated whenever operators' performance was below acceptable levels. Automation of the secondary task and transfer of the secondary task back to manual control were either human- or machine-initiated. Human-initiated transfers were based on the operator's assessment of the current task demands while machine-initiated transfers were based on the operators' performance. Different performance assessment methods were tested in two separate experiments.

Graph-based structural change detection for rotating machinery monitoring

NASA Astrophysics Data System (ADS)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

Acoustic emission from single point machining: Part 2, Signal changes with tool wear. Revised

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-12-31

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

PubMed Central

Fu, Longwen; Liu, Zuoyi

2018-01-01

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented. PMID:29849612

Torque shudder protection device and method

DOEpatents

King, Robert D.; De Doncker, Rik W. A. A.; Szczesny, Paul M.

1997-01-01

A torque shudder protection device for an induction machine includes a flux command generator for supplying a steady state flux command and a torque shudder detector for supplying a status including a negative status to indicate a lack of torque shudder and a positive status to indicate a presence of torque shudder. A flux adapter uses the steady state flux command and the status to supply a present flux command identical to the steady state flux command for a negative status and different from the steady state flux command for a positive status. A limiter can receive the present flux command, prevent the present flux command from exceeding a predetermined maximum flux command magnitude, and supply the present flux command to a field oriented controller. After determining a critical electrical excitation frequency at which a torque shudder occurs for the induction machine, a flux adjuster can monitor the electrical excitation frequency of the induction machine and adjust a flux command to prevent the monitored electrical excitation frequency from reaching the critical electrical excitation frequency.

Torque shudder protection device and method

DOEpatents

King, R.D.; Doncker, R.W.A.A. De.; Szczesny, P.M.

1997-03-11

A torque shudder protection device for an induction machine includes a flux command generator for supplying a steady state flux command and a torque shudder detector for supplying a status including a negative status to indicate a lack of torque shudder and a positive status to indicate a presence of torque shudder. A flux adapter uses the steady state flux command and the status to supply a present flux command identical to the steady state flux command for a negative status and different from the steady state flux command for a positive status. A limiter can receive the present flux command, prevent the present flux command from exceeding a predetermined maximum flux command magnitude, and supply the present flux command to a field oriented controller. After determining a critical electrical excitation frequency at which a torque shudder occurs for the induction machine, a flux adjuster can monitor the electrical excitation frequency of the induction machine and adjust a flux command to prevent the monitored electrical excitation frequency from reaching the critical electrical excitation frequency. 5 figs.

Automatic optical detection and classification of marine animals around MHK converters using machine vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brunton, Steven

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robustmore » principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.« less

Analysis of acoustic emission signals and monitoring of machining processes

PubMed

Govekar; Gradisek; Grabec

2000-03-01

Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.

An evaluation of retrofit engineering control interventions to reduce perchloroethylene exposures in commercial dry-cleaning shops.

PubMed

Earnest, G Scott; Ewers, Lynda M; Ruder, Avima M; Petersen, Martin R; Kovein, Ronald J

2002-02-01

Real-time monitoring was used to evaluate the ability of engineering control devices retrofitted on two existing dry-cleaning machines to reduce worker exposures to perchloroethylene. In one dry-cleaning shop, a refrigerated condenser was installed on a machine that had a water-cooled condenser to reduce the air temperature, improve vapor recovery, and lower exposures. In a second shop, a carbon adsorber was retrofitted on a machine to adsorb residual perchloroethylene not collected by the existing refrigerated condenser to improve vapor recovery and reduce exposures. Both controls were successful at reducing the perchloroethylene exposures of the dry-cleaning machine operator. Real-time monitoring was performed to evaluate how the engineering controls affected exposures during loading and unloading the dry-cleaning machine, a task generally considered to account for the highest exposures. The real-time monitoring showed that dramatic reductions occurred in exposures during loading and unloading of the dry-cleaning machine due to the engineering controls. Peak operator exposures during loading and unloading were reduced by 60 percent in the shop that had a refrigerated condenser installed on the dry-cleaning machine and 92 percent in the shop that had a carbon adsorber installed. Although loading and unloading exposures were dramatically reduced, drops in full-shift time-weighted average (TWA) exposures were less dramatic. TWA exposures to perchloroethylene, as measured by conventional air sampling, showed smaller reductions in operator exposures of 28 percent or less. Differences between exposure results from real-time and conventional air sampling very likely resulted from other uncontrolled sources of exposure, differences in shop general ventilation before and after the control was installed, relatively small sample sizes, and experimental variability inherent in field research. Although there were some difficulties and complications with installation and maintenance of the engineering controls, this study showed that retrofitting engineering controls may be a feasible option for some dry-cleaning shop owners to reduce worker exposures to perchloroethylene. By installing retrofit controls, a dry-cleaning facility can reduce exposures, in some cases dramatically, and bring operators into compliance with the Occupational Safety and Health Administration (OSHA) peak exposure limit of 300 ppm. Retrofit engineering controls are also likely to enable many dry-cleaning workers to lower their overall personal TWA exposures to perchloroethylene.

A Methodology for Protective Vibration Monitoring of Hydropower Units Based on the Mechanical Properties.

PubMed

Nässelqvist, Mattias; Gustavsson, Rolf; Aidanpää, Jan-Olov

2013-07-01

It is important to monitor the radial loads in hydropower units in order to protect the machine from harmful radial loads. Existing recommendations in the standards regarding the radial movements of the shaft and bearing housing in hydropower units, ISO-7919-5 (International Organization for Standardization, 2005, "ISO 7919-5: Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Rotating Shafts-Part 5: Machine Sets in Hydraulic Power Generating and Pumping Plants," Geneva, Switzerland) and ISO-10816-5 (International Organization for Standardization, 2000, "ISO 10816-5: Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Non-Rotating Parts-Part 5: Machine Sets in Hydraulic Power Generating and Pumping Plants," Geneva, Switzerland), have alarm levels based on statistical data and do not consider the mechanical properties of the machine. The synchronous speed of the unit determines the maximum recommended shaft displacement and housing acceleration, according to these standards. This paper presents a methodology for the alarm and trip levels based on the design criteria of the hydropower unit and the measured radial loads in the machine during operation. When a hydropower unit is designed, one of its design criteria is to withstand certain loads spectra without the occurrence of fatigue in the mechanical components. These calculated limits for fatigue are used to set limits for the maximum radial loads allowed in the machine before it shuts down in order to protect itself from damage due to high radial loads. Radial loads in hydropower units are caused by unbalance, shape deviations, dynamic flow properties in the turbine, etc. Standards exist for balancing and manufacturers (and power plant owners) have recommendations for maximum allowed shape deviations in generators. These standards and recommendations determine which loads, at a maximum, should be allowed before an alarm is sent that the machine needs maintenance. The radial bearing load can be determined using load cells, bearing properties multiplied by shaft displacement, or bearing bracket stiffness multiplied by housing compression or movement. Different load measurement methods should be used depending on the design of the machine and accuracy demands in the load measurement. The methodology presented in the paper is applied to a 40 MW hydropower unit; suggestions are presented for the alarm and trip levels for the machine based on the mechanical properties and radial loads.

Resolution Studies at Beam Position Monitors at the FLASH Facility at DESY

NASA Astrophysics Data System (ADS)

Baboi, N.; Lund-Nielsen, J.; Noelle, D.; Riesch, W.; Traber, T.; Kruse, J.; Wendt, M.

2006-11-01

More than 60 beam position monitors (BPM) are installed along about 350m of beamline of the Free Electron LASer in Hamburg (FLASH) at DESY. The room-temperature part of the accelerator is equipped mainly with stripline position monitors. In the accelerating cryo-modules there are cavity and re-entrant cavity BPMs, which will not be discussed here. In the undulator part of the machine button BPMs are used. This area requires a single bunch resolution of 10μm. The electronics is based on the AM/PM normalization principle and is externally triggered. Single-bunch position is measured. This paper presents the methods used to determine the resolution of the BPMs. The results based on correlations between different BPMs along the machine are compared to noise measurements in the RF lab. The performance and difficulties with the BPM design and the current electronics as well as its development are discussed.

Mobile machine hazardous working zone warning system

DOEpatents

Schiffbauer, William H.; Ganoe, Carl W.

1999-01-01

A warning system is provided for a mobile working machine to alert an individual of a potentially dangerous condition in the event the individual strays into a hazardous working zone of the machine. The warning system includes a transmitter mounted on the machine and operable to generate a uniform magnetic field projecting beyond an outer periphery of the machine in defining a hazardous working zone around the machine during operation thereof. A receiver, carried by the individual and activated by the magnetic field, provides an alarm signal to alert the individual when he enters the hazardous working zone of the machine.

Mobile machine hazardous working zone warning system

DOEpatents

Schiffbauer, W.H.; Ganoe, C.W.

1999-08-17

A warning system is provided for a mobile working machine to alert an individual of a potentially dangerous condition in the event the individual strays into a hazardous working zone of the machine. The warning system includes a transmitter mounted on the machine and operable to generate a uniform magnetic field projecting beyond an outer periphery of the machine in defining a hazardous working zone around the machine during operation. A receiver, carried by the individual and activated by the magnetic field, provides an alarm signal to alert the individual when he enters the hazardous working zone of the machine. 3 figs.

Machine Learning: Proceedings of the Fifteenth International Conference

DTIC Science & Technology

1998-07-01

Machine Learning Proceedings of the Fifteenth International Conference (ICML 󈨦) Edited by Jude Shavlik MADISON , WISCONSIN JULY 24-27, 1998 fc...W. Dayton Street Madison , WI 53706 PERFORMING ORGANIZATION REPORT NUMBER 144-HD17 9. SPONSORING /MONITORING AGENCY NAMES(S) AND ADDRESS(ES...ANISE Sad 239-18 298-102 University of Wisconsin - Madison Jude W. Shavlik Department of Computer Sciences Professor 1210 West Dayton Street

Energy Harvesting for Soft-Matter Machines and Electronics

DTIC Science & Technology

2016-06-09

AFRL-AFOSR-VA-TR-2016-0353 Energy Harvesting for Soft-Matter Machines and Electronics Carmel Majidi CARNEGIE MELLON UNIVERSITY Final Report 06/09...ES) CARNEGIE MELLON UNIVERSITY 5000 FORBES AVENUE PITTSBURGH, PA 15213-3815 US 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING...livelink.ebs.afrl.af.mil/livelink/llisapi.dll DISTRIBUTION A: Distribution approved for public release. Carnegie Mellon University MECHANICAL ENGINEERING FINAL

Quench monitoring and control system and method of operating same

DOEpatents

Ryan, David Thomas; Laskaris, Evangelos Trifon; Huang, Xianrui

2006-05-30

A rotating machine comprising a superconductive coil and a temperature sensor operable to provide a signal representative of superconductive coil temperature. The rotating machine may comprise a control system communicatively coupled to the temperature sensor. The control system may be operable to reduce electric current in the superconductive coil when a signal representative of a defined superconducting coil temperature is received from the temperature sensor.

Effective dust control systems on concrete dowel drilling machinery.

PubMed

Echt, Alan S; Sanderson, Wayne T; Mead, Kenneth R; Feng, H Amy; Farwick, Daniel R; Farwick, Dawn Ramsey

2016-09-01

Rotary-type percussion dowel drilling machines, which drill horizontal holes in concrete pavement, have been documented to produce respirable crystalline silica concentrations above recommended exposure criteria. This places operators at potential risk for developing health effects from exposure. United States manufacturers of these machines offer optional dust control systems. The effectiveness of the dust control systems to reduce respirable dust concentrations on two types of drilling machines was evaluated under controlled conditions with the machines operating inside large tent structures in an effort to eliminate secondary exposure sources not related to the dowel-drilling operation. Area air samples were collected at breathing zone height at three locations around each machine. Through equal numbers of sampling rounds with the control systems randomly selected to be on or off, the control systems were found to significantly reduce respirable dust concentrations from a geometric mean of 54 mg per cubic meter to 3.0 mg per cubic meter on one machine and 57 mg per cubic meter to 5.3 mg per cubic meter on the other machine. This research shows that the dust control systems can dramatically reduce respirable dust concentrations by over 90% under controlled conditions. However, these systems need to be evaluated under actual work conditions to determine their effectiveness in reducing worker exposures to crystalline silica below hazardous levels.

Ecophysiological Modeling of Grapevine Water Stress in Burgundy Terroirs by a Machine-Learning Approach.

PubMed

Brillante, Luca; Mathieu, Olivier; Lévêque, Jean; Bois, Benjamin

2016-01-01

In a climate change scenario, successful modeling of the relationships between plant-soil-meteorology is crucial for a sustainable agricultural production, especially for perennial crops. Grapevines (Vitis vinifera L. cv Chardonnay) located in eight experimental plots (Burgundy, France) along a hillslope were monitored weekly for 3 years for leaf water potentials, both at predawn (Ψpd) and at midday (Ψstem). The water stress experienced by grapevine was modeled as a function of meteorological data (minimum and maximum temperature, rainfall) and soil characteristics (soil texture, gravel content, slope) by a gradient boosting machine. Model performance was assessed by comparison with carbon isotope discrimination (δ(13)C) of grape sugars at harvest and by the use of a test-set. The developed models reached outstanding prediction performance (RMSE < 0.08 MPa for Ψstem and < 0.06 MPa for Ψpd), comparable to measurement accuracy. Model predictions at a daily time step improved correlation with δ(13)C data, respect to the observed trend at a weekly time scale. The role of each predictor in these models was described in order to understand how temperature, rainfall, soil texture, gravel content and slope affect the grapevine water status in the studied context. This work proposes a straight-forward strategy to simulate plant water stress in field condition, at a local scale; to investigate ecological relationships in the vineyard and adapt cultural practices to future conditions.

High-speed combustion diagnostics in a rapid compression machine by broadband supercontinuum absorption spectroscopy.

PubMed

Werblinski, Thomas; Fendt, Peter; Zigan, Lars; Will, Stefan

2017-05-20

The first results under fired internal combustion engine conditions based on a supercontinuum absorption spectrometer are presented and discussed. Temperature, pressure, and water mole fraction are inferred simultaneously from broadband H 2 O absorbance spectra ranging from 1340 nm to 1440 nm. The auto-ignition combustion process is monitored for two premixed n-heptane/air mixtures with 10 kHz in a rapid compression machine. Pressure and temperature levels during combustion exceed 65 bar and 1900 K, respectively. To allow for combustion measurements, the robustness of the spectrometer against beam steering has been improved compared to its previous version. Additionally, the detectable wavelength range has been extended further into the infrared region to allow for the acquisition of distinct high-temperature water transitions located in the P-branch above 1410 nm. Based on a theoretical study, line-of-sight (LOS) effects introduced by temperature stratification on the broadband fitting algorithm in the complete range from 1340 nm to 1440 nm are discussed. In this context, the recorded spectra during combustion were evaluated only within a narrower spectral region exhibiting almost no interference from low-temperature molecules (here, P-branch from 1410 nm to 1440 nm). It is shown that this strategy mitigates almost all of the LOS effects introduced by cold molecules and the evaluation of the spectrum in the entirely recorded wavelength range at engine combustion conditions.

Pre-Finishing of SiC for Optical Applications

NASA Technical Reports Server (NTRS)

Rozzi, Jay; Clavier, Odile; Gagne, John

2011-01-01

13 Manufacturing & Prototyping A method is based on two unique processing steps that are both based on deterministic machining processes using a single-point diamond turning (SPDT) machine. In the first step, a high-MRR (material removal rate) process is used to machine the part within several microns of the final geometry. In the second step, a low-MRR process is used to machine the part to near optical quality using a novel ductile regime machining (DRM) process. DRM is a deterministic machining process associated with conditions under high hydrostatic pressures and very small depths of cut. Under such conditions, using high negative-rake angle cutting tools, the high-pressure region near the tool corresponds to a plastic zone, where even a brittle material will behave in a ductile manner. In the high-MRR processing step, the objective is to remove material with a sufficiently high rate such that the process is economical, without inducing large-scale subsurface damage. A laser-assisted machining approach was evaluated whereby a CO2 laser was focused in advance of the cutting tool. While CVD (chemical vapor deposition) SiC was successfully machined with this approach, the cutting forces were substantially higher than cuts at room temperature under the same machining conditions. During the experiments, the expansion of the part and the tool due to the heating was carefully accounted for. The higher cutting forces are most likely due to a small reduction in the shear strength of the material compared with a larger increase in friction forces due to the thermal softening effect. The key advantage is that the hybrid machine approach has the potential to achieve optical quality without the need for a separate optical finishing step. Also, this method is scalable, so one can easily progress from machining 50-mm-diameter samples to the 250-mm-diameter mirror that NASA desires.

Machine Learning: A Crucial Tool for Sensor Design

PubMed Central

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E.

2009-01-01

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. PMID:20191110

Semi-automated Digital Imaging and Processing System for Measuring Lake Ice Thickness

NASA Astrophysics Data System (ADS)

Singh, Preetpal

Canada is home to thousands of freshwater lakes and rivers. Apart from being sources of infinite natural beauty, rivers and lakes are an important source of water, food and transportation. The northern hemisphere of Canada experiences extreme cold temperatures in the winter resulting in a freeze up of regional lakes and rivers. Frozen lakes and rivers tend to offer unique opportunities in terms of wildlife harvesting and winter transportation. Ice roads built on frozen rivers and lakes are vital supply lines for industrial operations in the remote north. Monitoring the ice freeze-up and break-up dates annually can help predict regional climatic changes. Lake ice impacts a variety of physical, ecological and economic processes. The construction and maintenance of a winter road can cost millions of dollars annually. A good understanding of ice mechanics is required to build and deem an ice road safe. A crucial factor in calculating load bearing capacity of ice sheets is the thickness of ice. Construction costs are mainly attributed to producing and maintaining a specific thickness and density of ice that can support different loads. Climate change is leading to warmer temperatures causing the ice to thin faster. At a certain point, a winter road may not be thick enough to support travel and transportation. There is considerable interest in monitoring winter road conditions given the high construction and maintenance costs involved. Remote sensing technologies such as Synthetic Aperture Radar have been successfully utilized to study the extent of ice covers and record freeze-up and break-up dates of ice on lakes and rivers across the north. Ice road builders often used Ultrasound equipment to measure ice thickness. However, an automated monitoring system, based on machine vision and image processing technology, which can measure ice thickness on lakes has not been thought of. Machine vision and image processing techniques have successfully been used in manufacturing to detect equipment failure and identify defective products at the assembly line. The research work in this thesis combines machine vision and image processing technology to build a digital imaging and processing system for monitoring and measuring lake ice thickness in real time. An ultra-compact USB camera is programmed to acquire and transmit high resolution imagery for processing with MATLAB Image Processing toolbox. The image acquisition and transmission process is fully automated; image analysis is semi-automated and requires limited user input. Potential design changes to the prototype and ideas on fully automating the imaging and processing procedure are presented to conclude this research work.

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

Spinal muscle activity in simulated rugby union scrummaging is affected by different engagement conditions.

PubMed

Cazzola, D; Stone, B; Holsgrove, T P; Trewartha, G; Preatoni, E

2016-04-01

Biomechanical studies of rugby union scrummaging have focused on kinetic and kinematic analyses, while muscle activation strategies employed by front-row players during scrummaging are still unknown. The aim of the current study was to investigate the activity of spinal muscles during machine and live scrums. Nine male front-row forwards scrummaged as individuals against a scrum machine under "crouch-touch-set" and "crouch-bind-set" conditions, and against a two-player opposition in a simulated live condition. Muscle activities of the sternocleidomastoid, upper trapezius, and erector spinae were measured over the pre-engagement, engagement, and sustained-push phases. The "crouch-bind-set" condition increased muscle activity of the upper trapezius and sternocleidomastoid before and during the engagement phase in machine scrummaging. During the sustained-push phase, live scrummaging generated higher activities of the erector spinae than either machine conditions. These results suggest that the pre-bind, prior to engagement, may effectively prepare the cervical spine by stiffening joints before the impact phase. Additionally, machine scrummaging does not replicate the muscular demands of live scrummaging for the erector spinae, and for this reason, we advise rugby union forwards to ensure scrummaging is practiced in live situations to improve the specificity of their neuromuscular activation strategies in relation to resisting external loads. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Ideology of a multiparametric system for estimating the insulation system of electric machines on the basis of absorption testing methods

NASA Astrophysics Data System (ADS)

Kislyakov, M. A.; Chernov, V. A.; Maksimkin, V. L.; Bozhin, Yu. M.

2017-12-01

The article deals with modern methods of monitoring the state and predicting the life of electric machines. In 50% of the cases of failure in the performance of electric machines is associated with insulation damage. As promising, nondestructive methods of control, methods based on the investigation of the processes of polarization occurring in insulating materials are proposed. To improve the accuracy of determining the state of insulation, a multiparametric approach is considered, which is a basis for the development of an expert system for estimating the state of health.

Uses of the Westrup brush machine

Treesearch

Jill Barbour

2002-01-01

The Westrup brush machine can be used as the first step in the conditioning process of seeds. Even though there are various sizes of the machine, only the laboratory model (LA-H) is described. The machine is designed to separate seed from pods or flowers, dewing tree seed, remove appendages or hairs from seed, split twin seed, de-lint cotton seed, scarify hard coated...

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

LHCb experience with running jobs in virtual machines

NASA Astrophysics Data System (ADS)

McNab, A.; Stagni, F.; Luzzi, C.

2015-12-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites managed using Vcycle/OpenStack, Vac, and HTCondor Vacuum. Finally we show how our use of these resources is monitored using Ganglia and DIRAC.

Ensembles of novelty detection classifiers for structural health monitoring using guided waves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dib, Gerges; Karpenko, Oleksii; Koricho, Ermias

Guided wave structural health monitoring uses sparse sensor networks embedded in sophisticated structures for defect detection and characterization. The biggest challenge of those sensor networks is developing robust techniques for reliable damage detection under changing environmental and operating conditions. To address this challenge, we develop a novelty classifier for damage detection based on one class support vector machines. We identify appropriate features for damage detection and introduce a feature aggregation method which quadratically increases the number of available training observations.We adopt a two-level voting scheme by using an ensemble of classifiers and predictions. Each classifier is trained on a differentmore » segment of the guided wave signal, and each classifier makes an ensemble of predictions based on a single observation. Using this approach, the classifier can be trained using a small number of baseline signals. We study the performance using monte-carlo simulations of an analytical model and data from impact damage experiments on a glass fiber composite plate.We also demonstrate the classifier performance using two types of baseline signals: fixed and rolling baseline training set. The former requires prior knowledge of baseline signals from all environmental and operating conditions, while the latter does not and leverages the fact that environmental and operating conditions vary slowly over time and can be modeled as a Gaussian process.« less

Cyclostationarity approach for monitoring chatter and tool wear in high speed milling

NASA Astrophysics Data System (ADS)

Lamraoui, M.; Thomas, M.; El Badaoui, M.

2014-02-01

Detection of chatter and tool wear is crucial in the machining process and their monitoring is a key issue, for: (1) insuring better surface quality, (2) increasing productivity and (3) protecting both machines and safe workpiece. This paper presents an investigation of chatter and tool wear using the cyclostationary method to process the vibrations signals acquired from high speed milling. Experimental cutting tests were achieved on slot milling operation of aluminum alloy. The experimental set-up is designed for acquisition of accelerometer signals and encoding information picked up from an encoder. The encoder signal is used for re-sampling accelerometers signals in angular domain using a specific algorithm that was developed in LASPI laboratory. The use of cyclostationary on accelerometer signals has been applied for monitoring chatter and tool wear in high speed milling. The cyclostationarity appears on average properties (first order) of signals, on the energetic properties (second order) and it generates spectral lines at cyclic frequencies in spectral correlation. Angular power and kurtosis are used to analyze chatter phenomena. The formation of chatter is characterized by unstable, chaotic motion of the tool and strong anomalous fluctuations of cutting forces. Results show that stable machining generates only very few cyclostationary components of second order while chatter is strongly correlated to cyclostationary components of second order. By machining in the unstable region, chatter results in flat angular kurtosis and flat angular power, such as a pseudo (white) random signal with flat spectrum. Results reveal that spectral correlation and Wigner Ville spectrum or integrated Wigner Ville issued from second-order cyclostationary are an efficient parameter for the early diagnosis of faults in high speed machining, such as chatter, tool wear and bearings, compared to traditional stationary methods. Wigner Ville representation of the residual signal shows that the energy corresponding to the tooth passing decreases when chatter phenomenon occurs. The effect of the tool wear and the number of broken teeth on the excitation of structure resonances appears in Wigner Ville presentation.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

All Sky Cloud Coverage Monitoring for SONG-China Project

NASA Astrophysics Data System (ADS)

Tian, J. F.; Deng, L. C.; Yan, Z. Z.; Wang, K.; Wu, Y.

2016-05-01

In order to monitor the cloud distributions at Qinghai station, a site selected for SONG (Stellar Observations Network Group)-China node, the design of the proto-type of all sky camera (ASC) applied in Xinglong station is adopted. Both hardware and software improvements have been made in order to be more precise and deliver quantitative measurements. The ARM (Advanced Reduced Instruction Set Computer Machine) MCU (Microcontroller Unit) instead of PC is used to control the upgraded version of ASC. A much higher reliability has been realized in the current scheme. Independent of the positions of the Sun and Moon, the weather conditions are constantly changing, therefore it is difficult to get proper exposure parameters using only the temporal information of the major light sources. A realistic exposure parameters for the ASC can actually be defined using a real-time sky brightness monitor that is also installed at the same site. The night sky brightness value is a very sensitive function of the cloud coverage, and can be accurately measured by the sky quality monitor. We study the correlation between the exposure parameter and night sky brightness value, and give the mathematical relation. The images of the all sky camera are inserted into database directly. All sky quality images are archived in FITS format which can be used for further analysis.

Optimizing cutting conditions on sustainable machining of aluminum alloy to minimize power consumption

NASA Astrophysics Data System (ADS)

Nur, Rusdi; Suyuti, Muhammad Arsyad; Susanto, Tri Agus

2017-06-01

Aluminum is widely utilized in the industrial sector. There are several advantages of aluminum, i.e. good flexibility and formability, high corrosion resistance and electrical conductivity, and high heat. Despite of these characteristics, however, pure aluminum is rarely used because of its lacks of strength. Thus, most of the aluminum used in the industrial sectors was in the form of alloy form. Sustainable machining can be considered to link with the transformation of input materials and energy/power demand into finished goods. Machining processes are responsible for environmental effects accepting to their power consumption. The cutting conditions have been optimized to minimize the cutting power, which is the power consumed for cutting. This paper presents an experimental study of sustainable machining of Al-11%Si base alloy that was operated without any cooling system to assess the capacity in reducing power consumption. The cutting force was measured and the cutting power was calculated. Both of cutting force and cutting power were analyzed and modeled by using the central composite design (CCD). The result of this study indicated that the cutting speed has an effect on machining performance and that optimum cutting conditions have to be determined, while sustainable machining can be followed in terms of minimizing power consumption and cutting force. The model developed from this study can be used for evaluation process and optimization to determine optimal cutting conditions for the performance of the whole process.

14 CFR 1260.57 - New technology.

Code of Federal Regulations, 2013 CFR

2013-01-01

... operate, in case of a machine or system; and, in each case, under such conditions as to establish that the... items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures, and...

14 CFR 1260.57 - New technology.

Code of Federal Regulations, 2012 CFR

2012-01-01

... operate, in case of a machine or system; and, in each case, under such conditions as to establish that the... items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures, and...

Configuration Management and Infrastructure Monitoring Using CFEngine and Icinga for Real-time Heterogeneous Data Taking Environment

NASA Astrophysics Data System (ADS)

Poat, M. D.; Lauret, J.; Betts, W.

2015-12-01

The STAR online computing environment is an intensive ever-growing system used for real-time data collection and analysis. Composed of heterogeneous and sometimes groups of custom-tuned machines, the computing infrastructure was previously managed by manual configurations and inconsistently monitored by a combination of tools. This situation led to configuration inconsistency and an overload of repetitive tasks along with lackluster communication between personnel and machines. Globally securing this heterogeneous cyberinfrastructure was tedious at best and an agile, policy-driven system ensuring consistency, was pursued. Three configuration management tools, Chef, Puppet, and CFEngine have been compared in reliability, versatility and performance along with a comparison of infrastructure monitoring tools Nagios and Icinga. STAR has selected the CFEngine configuration management tool and the Icinga infrastructure monitoring system leading to a versatile and sustainable solution. By leveraging these two tools STAR can now swiftly upgrade and modify the environment to its needs with ease as well as promptly react to cyber-security requests. By creating a sustainable long term monitoring solution, the detection of failures was reduced from days to minutes, allowing rapid actions before the issues become dire problems, potentially causing loss of precious experimental data or uptime.

Hydrogen exchange mass spectrometry of bacteriorhodopsin reveals light-induced changes in the structural dynamics of a biomolecular machine.

PubMed

Pan, Yan; Brown, Leonid; Konermann, Lars

2011-12-21

Many proteins act as molecular machines that are fuelled by a nonthermal energy source. Examples include transmembrane pumps and stator-rotor complexes. These systems undergo cyclic motions (CMs) that are being driven along a well-defined conformational trajectory. Superimposed on these CMs are thermal fluctuations (TFs) that are coupled to stochastic motions of the solvent. Here we explore whether the TFs of a molecular machine are affected by the occurrence of CMs. Bacteriorhodopsin (BR) is a light-driven proton pump that serves as a model system in this study. The function of BR is based on a photocycle that involves trans/cis isomerization of a retinal chromophore, as well as motions of transmembrane helices. Hydrogen/deuterium exchange (HDX) mass spectrometry was used to monitor the TFs of BR, focusing on the monomeric form of the protein. Comparative HDX studies were conducted under illumination and in the dark. The HDX kinetics of BR are dramatically accelerated in the presence of light. The isotope exchange rates and the number of backbone amides involved in EX2 opening transitions increase roughly 2-fold upon illumination. In contrast, light/dark control experiments on retinal-free protein produced no discernible differences. It can be concluded that the extent of TFs in BR strongly depends on photon-driven CMs. The light-induced differences in HDX behavior are ascribed to protein destabilization. Specifically, the thermodynamic stability of the dark-adapted protein is estimated to be 5.5 kJ mol(-1) under the conditions of our work. This value represents the free energy difference between the folded state F and a significantly unfolded conformer U. Illumination reduces the stability of F by 2.2 kJ mol(-1). Mechanical agitation caused by isomerization of the chromophore is transferred to the surrounding protein scaffold, and subsequently, the energy dissipates into the solvent. Light-induced retinal motions therefore act analogously to an internal heat source that promotes the occurrence of TFs. Overall, our data highlight the potential of HDX methods for probing the structural dynamics of molecular machines under "engine on" and "engine off" conditions. © 2011 American Chemical Society

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Pulsed ultrasonic instruments for monitoring the strength of materials

NASA Astrophysics Data System (ADS)

Korolev, Mikhail Viktorovich; Starikov, Boris Pavlovich; Karpel'Son, Arkadii Efimovich

The book is concerned with various aspects of the design of portable instruments for the ultrasonic monitoring of the strength and ductile characteristics of structural materials, including metals, alloys, and ceramics. Particular attention is given to methods for increasing the accuracy of the instruments while reducing their size, which is particularly important in the design of miniature instruments for the real-time monitoring of machines, mechanisms, and structures during operation.

Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology

PubMed Central

Ellis, Daniel P. W.; Pérez, Jonathan H.; Wingfield, John C.; Boelman, Natalie T.

2018-01-01

Bioacoustic networks could vastly expand the coverage of wildlife monitoring to complement satellite observations of climate and vegetation. This approach would enable global-scale understanding of how climate change influences phenomena such as migratory timing of avian species. The enormous data sets that autonomous recorders typically generate demand automated analyses that remain largely undeveloped. We devised automated signal processing and machine learning approaches to estimate dates on which songbird communities arrived at arctic breeding grounds. Acoustically estimated dates agreed well with those determined via traditional surveys and were strongly related to the landscape’s snow-free dates. We found that environmental conditions heavily influenced daily variation in songbird vocal activity, especially before egg laying. Our novel approaches demonstrate that variation in avian migratory arrival can be detected autonomously. Large-scale deployment of this innovation in wildlife monitoring would enable the coverage necessary to assess and forecast changes in bird migration in the face of climate change. PMID:29938220

A haptic-inspired audio approach for structural health monitoring decision-making

NASA Astrophysics Data System (ADS)

Mao, Zhu; Todd, Michael; Mascareñas, David

2015-03-01

Haptics is the field at the interface of human touch (tactile sensation) and classification, whereby tactile feedback is used to train and inform a decision-making process. In structural health monitoring (SHM) applications, haptic devices have been introduced and applied in a simplified laboratory scale scenario, in which nonlinearity, representing the presence of damage, was encoded into a vibratory manual interface. In this paper, the "spirit" of haptics is adopted, but here ultrasonic guided wave scattering information is transformed into audio (rather than tactile) range signals. After sufficient training, the structural damage condition, including occurrence and location, can be identified through the encoded audio waveforms. Different algorithms are employed in this paper to generate the transformed audio signals and the performance of each encoding algorithms is compared, and also compared with standard machine learning classifiers. In the long run, the haptic decision-making is aiming to detect and classify structural damages in a more rigorous environment, and approaching a baseline-free fashion with embedded temperature compensation.

Evaluating the Security of Machine Learning Algorithms

DTIC Science & Technology

2008-05-20

Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a...computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions... machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a

Modeling of Principal Flank Wear: An Empirical Approach Combining the Effect of Tool, Environment and Workpiece Hardness

NASA Astrophysics Data System (ADS)

Mia, Mozammel; Al Bashir, Mahmood; Dhar, Nikhil Ranjan

2016-10-01

Hard turning is increasingly employed in machining, lately, to replace time-consuming conventional turning followed by grinding process. An excessive amount of tool wear in hard turning is one of the main hurdles to be overcome. Many researchers have developed tool wear model, but most of them developed it for a particular work-tool-environment combination. No aggregate model is developed that can be used to predict the amount of principal flank wear for specific machining time. An empirical model of principal flank wear (VB) has been developed for the different hardness of workpiece (HRC40, HRC48 and HRC56) while turning by coated carbide insert with different configurations (SNMM and SNMG) under both dry and high pressure coolant conditions. Unlike other developed model, this model includes the use of dummy variables along with the base empirical equation to entail the effect of any changes in the input conditions on the response. The base empirical equation for principal flank wear is formulated adopting the Exponential Associate Function using the experimental results. The coefficient of dummy variable reflects the shifting of the response from one set of machining condition to another set of machining condition which is determined by simple linear regression. The independent cutting parameters (speed, rate, depth of cut) are kept constant while formulating and analyzing this model. The developed model is validated with different sets of machining responses in turning hardened medium carbon steel by coated carbide inserts. For any particular set, the model can be used to predict the amount of principal flank wear for specific machining time. Since the predicted results exhibit good resemblance with experimental data and the average percentage error is <10 %, this model can be used to predict the principal flank wear for stated conditions.

Manufacturing process applications team (MATEAM). [technology transfer in the areas of machine tools and robots

NASA Technical Reports Server (NTRS)

1979-01-01

The transfer of NASA technology to the industrial sector is reported. Presentations to the machine tool and robot industries and direct technology transfers of the Adams Manipulator arm, a-c motor control, and the bolt tension monitor are discussed. A listing of proposed RTOP programs with strong potential is included. A detailed description of the rotor technology available to industry is given.

Explosion Monitoring with Machine Learning: A LSTM Approach to Seismic Event Discrimination

NASA Astrophysics Data System (ADS)

Magana-Zook, S. A.; Ruppert, S. D.

2017-12-01

The streams of seismic data that analysts look at to discriminate natural from man- made events will soon grow from gigabytes of data per day to exponentially larger rates. This is an interesting problem as the requirement for real-time answers to questions of non-proliferation will remain the same, and the analyst pool cannot grow as fast as the data volume and velocity will. Machine learning is a tool that can solve the problem of seismic explosion monitoring at scale. Using machine learning, and Long Short-term Memory (LSTM) models in particular, analysts can become more efficient by focusing their attention on signals of interest. From a global dataset of earthquake and explosion events, a model was trained to recognize the different classes of events, given their spectrograms. Optimal recurrent node count and training iterations were found, and cross validation was performed to evaluate model performance. A 10-fold mean accuracy of 96.92% was achieved on a balanced dataset of 30,002 instances. Given that the model is 446.52 MB it can be used to simultaneously characterize all incoming signals by researchers looking at events in isolation on desktop machines, as well as at scale on all of the nodes of a real-time streaming platform. LLNL-ABS-735911

Numerical Simulation of Earth Pressure on Head Chamber of Shield Machine with FEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li Shouju; Kang Chengang; Sun, Wei

2010-05-21

Model parameters of conditioned soils in head chamber of shield machine are determined based on tree-axial compression tests in laboratory. The loads acting on tunneling face are estimated according to static earth pressure principle. Based on Duncan-Chang nonlinear elastic constitutive model, the earth pressures on head chamber of shield machine are simulated in different aperture ratio cases for rotating cutterhead of shield machine. Relationship between pressure transportation factor and aperture ratio of shield machine is proposed by using aggression analysis.

48 CFR 1852.227-70 - New technology.

Code of Federal Regulations, 2011 CFR

2011-10-01

... method; or to operate, in case of a machine or system; and, in each case, under such conditions as to... contract. Reportable items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures...

14 CFR § 1260.57 - New technology.

Code of Federal Regulations, 2014 CFR

2014-01-01

... operate, in case of a machine or system; and, in each case, under such conditions as to establish that the... items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures, and...

Robust snow avalanche detection using machine learning on infrasonic array data

NASA Astrophysics Data System (ADS)

Thüring, Thomas; Schoch, Marcel; van Herwijnen, Alec; Schweizer, Jürg

2014-05-01

Snow avalanches may threaten people and infrastructure in mountain areas. Automated detection of avalanche activity would be highly desirable, in particular during times of poor visibility, to improve hazard assessment, but also to monitor the effectiveness of avalanche control by explosives. In the past, a variety of remote sensing techniques and instruments for the automated detection of avalanche activity have been reported, which are based on radio waves (radar), seismic signals (geophone), optical signals (imaging sensor) or infrasonic signals (microphone). Optical imagery enables to assess avalanche activity with very high spatial resolution, however it is strongly weather dependent. Radar and geophone-based detection typically provide robust avalanche detection for all weather conditions, but are very limited in the size of the monitoring area. On the other hand, due to the long propagation distance of infrasound through air, the monitoring area of infrasonic sensors can cover a large territory using a single sensor (or an array). In addition, they are by far more cost effective than radars or optical imaging systems. Unfortunately, the reliability of infrasonic sensor systems has so far been rather low due to the strong variation of ambient noise (e.g. wind) causing a high false alarm rate. We analyzed the data collected by a low-cost infrasonic array system consisting of four sensors for the automated detection of avalanche activity at Lavin in the eastern Swiss Alps. A comparably large array aperture (~350m) allows highly accurate time delay estimations of signals which arrive at different times at the sensors, enabling precise source localization. An array of four sensors is sufficient for the time resolved source localization of signals in full 3D space, which is an excellent method to anticipate true avalanche activity. Robust avalanche detection is then achieved by using machine learning methods such as support vector machines. The system is initially trained by using characteristic data features from known avalanche and non-avalanche events. Data features are obtained from output signals of the source localization algorithm or from Fourier or time domain processing and support the learning phase of the system. A significantly improved detection rate as well as a reduction of the false alarm rate was achieved compared to previous approaches.

Surface Preparation Methods to Enhance Dynamic Surface Property Measurements of Shocked Metal Surfaces

NASA Astrophysics Data System (ADS)

Zellner, Michael; McNeil, Wendy; Gray, George, III; Huerta, David; King, Nicholas; Neal, George; Payton, Jeremy; Rubin, Jim; Stevens, Gerald; Turley, William; Buttler, William

2008-03-01

This effort investigates surface-preparation methods to enhance dynamic surface-property measurements of shocked metal surfaces. To assess the ability of making reliable and consistent dynamic surface-property measurements, the amount of material ejected from the free-surface upon shock release to vacuum (ejecta) was monitored for shocked Al-1100 and Sn targets. Four surface preparation methods were considered: fly-cut machined finish, diamond-turned machine finish, polished finish, and ball-rolled. The samples were shock loaded by in-contact detonation of HE PBX-9501 on the front-side of the metal coupons. Ejecta production at the back-side or free-side of the metal coupons was monitored using piezoelectric pins, optical shadowgraphy, and x-ray attenuation radiography.

Surface preparation methods to enhance dynamic surface property measurements of shocked metal surfaces

NASA Astrophysics Data System (ADS)

Zellner, M. B.; Vogan McNeil, W.; Gray, G. T.; Huerta, D. C.; King, N. S. P.; Neal, G. E.; Valentine, S. J.; Payton, J. R.; Rubin, J.; Stevens, G. D.; Turley, W. D.; Buttler, W. T.

2008-04-01

This effort investigates surface-preparation methods to enhance dynamic surface-property measurements of shocked metal surfaces. To assess the ability of making reliable and consistent dynamic surface-property measurements, the amount of material ejected from the free surface upon shock release to vacuum (ejecta) was monitored for shocked Al-1100 and Sn targets. Four surface-preparation methods were considered: Fly-cut machine finish, diamond-turned machine finish, polished finish, and ball rolled. The samples were shock loaded by in-contact detonation of HE PBX-9501 on the front side of the metal coupons. Ejecta production at the back side or free side of the metal coupons was monitored using piezoelectric pins, optical shadowgraphy, and x-ray attenuation radiography.

Mass-spectrometric monitoring of the intravenous anesthetic concentration in the breathing circuit of an anesthesia machine

NASA Astrophysics Data System (ADS)

Elizarov, A. Yu.; Levshankov, A. I.

2011-04-01

Interaction between inhalational anesthetic sevoflurane and an absorber of CO2 (soda lime) in the breathing circuit of an anesthesia machine during low-flow anesthesia (0.5 l of a fresh gaseous mixture per minute) is studied with the mass-spectrometric method. Monitoring data for the concentration of sevoflurane and three toxic products of sevoflurane decompositions (substances A, B, and C) during anesthesia in the inspiration-expiration regime are presented. The highest concentration of substance A is found to be 65 ppm. The biochemical blood analysis before and after anesthesia shows that nephropathy is related to the function of liver toxicity. It is found that inhalational anesthetic sevoflurane influences the concentration of intravenous hypnotic propofol in blood.

Predicting the dissolution kinetics of silicate glasses using machine learning

NASA Astrophysics Data System (ADS)

Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

2018-05-01

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

Present and Future of M2M

NASA Astrophysics Data System (ADS)

Ono, Satoru; Watanabe, Takashi

In recent years, the rapid progress in the development of hardware and software technologies enables tiny and low cost information devices hereinafter referred to as Machine to be widely available. M2M (Machine to Machine) has been of much attention where many tiny machines are connected to each other through networks with minimal human intervention to provide smooth and intelligent management. M2M is a promising core technology providing timely, flexible, efficient and comprehensive service at low cost. M2M has wide variety of applications including energy management system, environmental monitoring system, intelligent transport system, industrial automation system and other applications. M2M consists of terminals and networks that connect them. In this paper, we mainly focus on M2M networking and mention the future direction of the technology.

The ASSERT Virtual Machine Kernel: Support for Preservation of Temporal Properties

NASA Astrophysics Data System (ADS)

Zamorano, J.; de la Puente, J. A.; Pulido, J. A.; Urueña

2008-08-01

A new approach to building embedded real-time software has been developed in the ASSERT project. One of its key elements is the concept of a virtual machine preserving the non-functional properties of the system, and especially real-time properties, all the way down from high- level design models down to executable code. The paper describes one instance of the virtual machine concept that provides support for the preservation of temporal properties both at the source code level —by accept- ing only "legal" entities, i.e. software components with statically analysable real-tim behaviour— and at run-time —by monitoring the temporal behaviour of the system. The virtual machine has been validated on several pilot projects carried out by aerospace companies in the framework of the ASSERT project.

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

Ergonomics for enhancing detection of machine abnormalities.

PubMed

Illankoon, Prasanna; Abeysekera, John; Singh, Sarbjeet

2016-10-17

Detecting abnormal machine conditions is of great importance in an autonomous maintenance environment. Ergonomic aspects can be invaluable when detection of machine abnormalities using human senses is examined. This research outlines the ergonomic issues involved in detecting machine abnormalities and suggests how ergonomics would improve such detections. Cognitive Task Analysis was performed in a plant in Sri Lanka where Total Productive Maintenance is being implemented to identify sensory types that would be used to detect machine abnormalities and relevant Ergonomic characteristics. As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.

Electromagnetic interference in intraoperative monitoring of motor evoked potentials and a wireless solution.

PubMed

Farajidavar, Aydin; Seifert, Jennifer L; Delgado, Mauricio R; Sparagana, Steven; Romero-Ortega, Mario I; Chiao, J-C

2016-02-01

Intraoperative neurophysiological monitoring (IONM) is utilized to minimize neurological morbidity during spine surgery. Transcranial motor evoked potentials (TcMEPs) are principal IONM signals in which the motor cortex of the subject is stimulated with electrical pulses and the evoked potentials are recorded from the muscles of interest. Currently available monitoring systems require the connection of 40-60 lengthy lead wires to the patient. These wires contribute to a crowded and cluttered surgical environment, and limit the maneuverability of the surgical team. In this work, it was demonstrated that the cumbersome wired system is vulnerable to electromagnetic interference (EMI) produced by operating room (OR) equipment. It was hypothesized that eliminating the lengthy recording wires can remove the EMI induced in the IONM signals. Hence, a wireless system to acquire TcMEPs was developed and validated through bench-top and animal experiments. Side-by-side TcMEPs acquisition from the wired and wireless systems in animal experiments under controlled conditions (absence of EMI from OR equipment) showed comparable magnitudes and waveforms, thus demonstrating the fidelity in the signal acquisition of the wireless solution. The robustness of the wireless system to minimize EMI was compared with a wired-system under identical conditions. Unlike the wired-system, the wireless system was not influenced by the electromagnetic waves from the C-Arm X-ray machine and temperature management system in the OR. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

29 CFR 1926.303 - Abrasive wheels and tools.

Code of Federal Regulations, 2013 CFR

2013-07-01

... and tools. (a) Power. All grinding machines shall be supplied with sufficient power to maintain the spindle speed at safe levels under all conditions of normal operation. (b) Guarding. (1) Grinding machines..., nut, and outer flange may be exposed on machines designed as portable saws. (c) Use of abrasive wheels...

29 CFR 1926.303 - Abrasive wheels and tools.

Code of Federal Regulations, 2014 CFR

2014-07-01

... and tools. (a) Power. All grinding machines shall be supplied with sufficient power to maintain the spindle speed at safe levels under all conditions of normal operation. (b) Guarding. (1) Grinding machines..., nut, and outer flange may be exposed on machines designed as portable saws. (c) Use of abrasive wheels...

29 CFR 1926.303 - Abrasive wheels and tools.

Code of Federal Regulations, 2012 CFR

2012-07-01

... and tools. (a) Power. All grinding machines shall be supplied with sufficient power to maintain the spindle speed at safe levels under all conditions of normal operation. (b) Guarding. (1) Grinding machines..., nut, and outer flange may be exposed on machines designed as portable saws. (c) Use of abrasive wheels...

Automatic food intake detection based on swallowing sounds.

PubMed

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-11-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions.

Automatic food intake detection based on swallowing sounds

PubMed Central

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-01-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions. PMID:23125873

Machining and grinding: High rate deformation in practice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Follansbee, P.S.

1993-04-01

Machining and grinding are well-established material-working operations involving highly non-uniform deformation and failure processes. A typical machining operation is characterized by uncertain boundary conditions (e.g.,surface interactions), three-dimensional stress states, large strains, high strain rates, non-uniform temperatures, highly localized deformations, and failure by both nominally ductile and brittle mechanisms. While machining and grinding are thought to be dominated by empiricism, even a cursory inspection leads one to the conclusion that this results more from necessity arising out of the complicated and highly interdisciplinary nature of the processes than from the lack thereof. With these conditions in mind, the purpose of thismore » paper is to outline the current understanding of strain rate effects in metals.« less

Machining and grinding: High rate deformation in practice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Follansbee, P.S.

1993-01-01

Machining and grinding are well-established material-working operations involving highly non-uniform deformation and failure processes. A typical machining operation is characterized by uncertain boundary conditions (e.g.,surface interactions), three-dimensional stress states, large strains, high strain rates, non-uniform temperatures, highly localized deformations, and failure by both nominally ductile and brittle mechanisms. While machining and grinding are thought to be dominated by empiricism, even a cursory inspection leads one to the conclusion that this results more from necessity arising out of the complicated and highly interdisciplinary nature of the processes than from the lack thereof. With these conditions in mind, the purpose of thismore » paper is to outline the current understanding of strain rate effects in metals.« less

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor.

PubMed

Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Kim, Hee-Young; Park, Daihee; Yoon, Sukhan

2017-01-29

Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect-using electric current shape analysis-for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between "does-not-need-to-be-replaced" and "needs-to-be-replaced" shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.

Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor

PubMed Central

Sa, Jaewon; Choi, Younchang; Chung, Yongwha; Kim, Hee-Young; Park, Daihee; Yoon, Sukhan

2017-01-01

Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification. PMID:28146057

Experimental Investigation of Minimum Quantity Lubrication in Meso-scale Milling with Varying Tool Diameter

NASA Astrophysics Data System (ADS)

Yusof, M. Q. M.; Harun, H. N. S. B.; Bahar, R.

2018-01-01

Minimum quantity lubrication (MQL) is a method that uses a very small amount of liquid to reduce friction between cutting tool and work piece during machining. The implementation of MQL machining has become a viable alternative to flood cooling machining and dry machining. The overall performance has been evaluated during meso-scale milling of mild steel using different diameter milling cutters. Experiments have been conducted under two different lubrication condition: dry and MQL with variable cutting parameters. The tool wear and its surface roughness, machined surfaces microstructure and surface roughness were observed for both conditions. It was found from the results that MQL produced better results compared to dry machining. The 0.5 mm tool has been selected as the most optimum tool diameter to be used with the lowest surface roughness as well as the least flank wear generation. For the workpiece, it was observed that the cutting temperature possesses crucial effect on the microstructure and the surface roughness of the machined surface and bigger diameter tool actually resulted in higher surface roughness. The poor conductivity of the cutting tool may be one of reasons behind.

Human Factors and Robotics: Current Status and Future Prospects.

DTIC Science & Technology

1981-10-01

relatively simple "pick and place" machines which have mechanical arms and hands for transferring workpieces, and may be reprogrammable . Japan’s...tasks can consist of self-monitoring of activity or the control of other machines. Spot welding in the manufacture of automobiles represents probably...application alone. On an automobile production line, the robot must be able to remember several different body styles (e.g., 2-door versus 4-door) with

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

PubMed Central

Arriandiaga, Ander; Portillo, Eva; Sánchez, Jose A.; Cabanes, Itziar; Pombo, Iñigo

2014-01-01

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations. PMID:24854055

Decentralized real-time simulation of forest machines

NASA Astrophysics Data System (ADS)

Freund, Eckhard; Adam, Frank; Hoffmann, Katharina; Rossmann, Juergen; Kraemer, Michael; Schluse, Michael

2000-10-01

To develop realistic forest machine simulators is a demanding task. A useful simulator has to provide a close- to-reality simulation of the forest environment as well as the simulation of the physics of the vehicle. Customers demand a highly realistic three dimensional forestry landscape and the realistic simulation of the complex motion of the vehicle even in rough terrain in order to be able to use the simulator for operator training under close-to- reality conditions. The realistic simulation of the vehicle, especially with the driver's seat mounted on a motion platform, greatly improves the effect of immersion into the virtual reality of a simulated forest and the achievable level of education of the driver. Thus, the connection of the real control devices of forest machines to the simulation system has to be supported, i.e. the real control devices like the joysticks or the board computer system to control the crane, the aggregate etc. Beyond, the fusion of the board computer system and the simulation system is realized by means of sensors, i.e. digital and analog signals. The decentralized system structure allows several virtual reality systems to evaluate and visualize the information of the control devices and the sensors. So, while the driver is practicing, the instructor can immerse into the same virtual forest to monitor the session from his own viewpoint. In this paper, we are describing the realized structure as well as the necessary software and hardware components and application experiences.

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

NASA Astrophysics Data System (ADS)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Measurements of the Rotation of the Flagellar Motor by Bead Assay.

PubMed

Kasai, Taishi; Sowa, Yoshiyuki

2017-01-01

The bacterial flagellar motor is a reversible rotary nano-machine powered by the ion flux across the cytoplasmic membrane. Each motor rotates a long helical filament that extends from the cell body at several hundreds revolutions per second. The output of the motor is characterized by its generated torque and rotational speed. The torque can be calculated as the rotational frictional drag coefficient multiplied by the angular velocity. Varieties of methods, including a bead assay, have been developed to measure the flagellar rotation rate under various load conditions on the motor. In this chapter, we describe a method to monitor the motor rotation through a position of a 1 μm bead attached to a truncated flagellar filament.

A walking prescription for statically-stable walkers based on walker/terrain interaction

NASA Technical Reports Server (NTRS)

Nagy, Peter V.; Whittaker, William L.; Desa, Subhas

1992-01-01

The walker/terrain interaction phenomena for the control of a statically stable walking machine are described. The algorithms, measures, and knowledge of walker/terrain interaction phenomena are then combined to form a prescription for how to walk on general terrain. This prescription consists of two parts: nominal control and reactive control. The function of nominal control is the evaluation and execution of planned motions, based on predicted foot force redistributions, to achieve reliable locomotion. The function of reactive control is the monitoring of walker/terrain interaction in real-time to detect anomalous conditions and then respond with the appropriate reflexive actions. Simulations and experiments have been used to test and verify various aspects of the walking prescription.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

PubMed

Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

2016-08-01

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

NASA Astrophysics Data System (ADS)

Zhang, Caiyun

2015-06-01

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, Object-based Image Analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetry data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Tailoring Earth Observation To Ranchers For Improved Land Management And Profitability: The VegMachine Online Project

NASA Astrophysics Data System (ADS)

Scarth, P.; Trevithick, B.; Beutel, T.

2016-12-01

VegMachine Online is a freely available browser application that allows ranchers across Australia to view and interact with satellite derived ground cover state and change maps on their property and extract this information in a graphical format using interactive tools. It supports the delivery and communication of a massive earth observation data set in an accessible, producer friendly way . Around 250,000 Landsat TM, ETM and OLI images were acquired across Australia, converted to terrain corrected surface reflectance and masked for cloud, cloud shadow, terrain shadow and water. More than 2500 field sites across the Australian rangelands were used to derive endmembers used in a constrained unmixing approach to estimate the per-pixel proportion of bare, green and non-green vegetation for all images. A seasonal metoid compositing method was used to produce national fractional cover virtual mosaics for each three month period since 1988. The time series of green fraction is used to estimate the persistent green due to tree and shrub canopies, and this estimate is used to correct the fractional cover to ground cover for our mixed tree-grass rangeland systems. Finally, deciles are produced for key metrics every season to track a pixels relativity to the entire time series. These data are delivered through time series enabled web mapping services and customised web processing services that enable the full time series over any spatial extent to be interrogated in seconds via a RESTful interface. These services interface with a front end browser application that provides product visualization for any date in the time series, tools to draw or import polygon boundaries, plot time series ground cover comparisons, look at the effect of historical rainfall and tools to run the revised universal soil loss equation in web time to assess the effect of proposed changes in cover retention. VegMachine Online is already being used by ranchers monitoring paddock condition, organisations supporting land management initiatives in Great Barrier Reef catchments, by students developing tools to understand land condition and degradation and the underlying data and APIs are supporting several other land condition mapping tools.

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

PubMed

Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P

2015-01-01

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

Needs of ergonomic design at control units in production industries.

PubMed

Levchuk, I; Schäfer, A; Lang, K-H; Gebhardt, Hj; Klussmann, A

2012-01-01

During the last decades, an increasing use of innovative technologies in manufacturing areas was monitored. A huge amount of physical workload was replaced by the change from conventional machine tools to computer-controlled units. CNC systems spread in current production processes. Because of this, machine operators today mostly have an observational function. This caused increasing of static work (e.g., standing, sitting) and cognitive demands (e.g., process observation). Machine operators have a high responsibility, because mistakes may lead to human injuries as well as to product losses - and in consequence may lead to high monetary losses (for the company) as well. Being usable often means for a CNC machine being efficient. An intuitive usability and an ergonomic organization of CNC workplaces can be an essential basis to reduce the risk of failures in operation as well as physical complaints (e.g. pain or diseases because of bad body posture during work). In contrast to conventional machines, CNC machines are equipped both with hardware and software. An intuitive and clear-sighted operating of CNC systems is a requirement for quick learning of new systems. Within this study, a survey was carried out among trainees learning the operation of CNC machines.

Correlation between use time of machine and decline curve for emerging enterprise information systems

NASA Astrophysics Data System (ADS)

Chang, Yao-Chung; Lai, Chin-Feng; Chuang, Chi-Cheng; Hou, Cheng-Yu

2018-04-01

With the progress of science and technology, more and more machines are adpot to help human life better and more convenient. When the machines have been used for a longer period of time so that the machine components are getting old, the amount of power comsumption will increase and easily cause the machine to overheat. This also causes a waste of invisible resources. If the Internet of Everything (IoE) technologies are able to be applied into the enterprise information systems for monitoring the machines use time, it can not only make energy can be effectively used, but aslo create a safer living environment. To solve the above problem, the correlation predict model is established to collect the data of power consumption converted into power eigenvalues. This study takes the power eigenvalue as the independent variable and use time as the dependent variable in order to establish the decline curve. Ultimately, the scoring and estimation modules are employed to seek the best power eigenvalue as the independent variable. To predict use time, the correlation is discussed between the use time and the decline curve to improve the entire behavioural analysis of the facilitate recognition of the use time of machines.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

Effects of machining conditions on the specific cutting energy of carbon fibre reinforced polymer composites

NASA Astrophysics Data System (ADS)

Azmi, A. I.; Syahmi, A. Z.; Naquib, M.; Lih, T. C.; Mansor, A. F.; Khalil, A. N. M.

2017-10-01

This article presents an approach to evaluate the effects of different machining conditions on the specific cutting energy of carbon fibre reinforced polymer composites (CFRP). Although research works in the machinability of CFRP composites have been very substantial, the present literature rarely discussed the topic of energy consumption and the specific cutting energy. A series of turning experiments were carried out on two different CFRP composites in order to determine the power and specific energy constants and eventually evaluate their effects due to the changes in machining conditions. A good agreement between the power and material removal rate using a simple linear relationship. Further analyses revealed that a power law function is best to describe the effect of feed rate on the changes in the specific cutting energy. At lower feed rate, the specific cutting energy increases exponentially due to the nature of finishing operation, whereas at higher feed rate, the changes in specific cutting energy is minimal due to the nature of roughing operation.

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision

PubMed Central

Wu, Dung-Sheng

2018-01-01

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time. PMID:29565303

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision.

PubMed

Ho, Chao-Ching; Wu, Dung-Sheng

2018-03-22

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

PubMed

Wang, Hui; Qin, Feng; Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

2016-01-01

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

PubMed Central

Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

2016-01-01

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. PMID:27128464

An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud

NASA Astrophysics Data System (ADS)

Shenbaga Moorthy, Rajalakshmi; Fareentaj, U.; Divya, T. K.

2017-08-01

Cloud computing provides an effective way to dynamically provide numerous resources to meet customer demands. A major challenging problem for cloud providers is designing efficient mechanisms for optimal virtual machine Placement (OVMP). Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. In order to provide appropriate resources to the clients an optimal virtual machine placement algorithm is proposed. Virtual machine placement is NP-Hard problem. Such NP-Hard problem can be solved using heuristic algorithm. In this paper, Ant Colony Optimization based virtual machine placement is proposed. Our proposed system focuses on minimizing the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment and the response time of each cloud provider is monitored periodically, in such a way to minimize delay in providing the resources to the users. The performance of the proposed algorithm is compared with greedy mechanism. The proposed algorithm is simulated in Eclipse IDE. The results clearly show that the proposed algorithm minimizes the cost, response time and also number of migrations.

Vibration monitoring via nano-composite piezoelectric foam bushings

NASA Astrophysics Data System (ADS)

Bird, Evan T.; Merrell, A. Jake; Anderson, Brady K.; Newton, Cory N.; Rosquist, Parker G.; Fullwood, David T.; Bowden, Anton E.; Seeley, Matthew K.

2016-11-01

Most mechanical systems produce vibrations as an inherent side effect of operation. Though some vibrations are acceptable in operation, others can cause damage or signal a machine’s imminent failure. These vibrations would optimally be monitored in real-time, without human supervision to prevent failure and excessive wear in machinery. This paper explores a new alternative to currently-used machine-monitoring equipment, namely a piezoelectric foam sensor system. These sensors are made of a silicone-based foam embedded with nano- and micro-scale conductive particles. Upon impact, they emit an electric response that is directly correlated with impact energy, with no electrical power input. In the present work, we investigated their utility as self-sensing bushings on machinery. These sensors were found to accurately detect both the amplitude and frequency of typical machine vibrations. The bushings could potentially save time and money over other vibration sensing mechanisms, while simultaneously providing a potential control input that could be utilized for correcting vibrational imbalance.

[A field study on the work load and muscle fatigue at neck-shoulder in female sewing machine operators by using surface electromyography].

PubMed

Zhang, Fei-ruo; Wang, Sheng; He, Li-hua; Zhang, Ying; Wu, Shan-shan; Li, Jing-yun; Hu, Guang-yi; Ye, Kang-ping

2011-03-01

To study neck and shoulder work-related muscle fatigue of female sewing machine operators. 18 health female sewing machine operators without musculoskeletal disorders work in Beijing garment industry factory as volunteers in participate of this study. The maximal voluntary contraction (MVC) and 20% MVC of bilateral upper trapezium and cervical erectors spinae was tested before sewing operations, then the whole 20 time windows (1 time window = 10 min) sewing machine operations was monitored and the surface electromyography (sEMG) signals simultaneously was recorded after monitoring the 20%MVC was tested. Use amplitude analysis method to reduction recorded EMG signals. During work, the median load for the left cervical erector spinae (LCES), right cervical erector spinae (RCES), left upper trapezium (LUT) and right upper trapezium (RUT) respectively was 6.78 ± 1.05, 6.94 ± 1.12, 5.68 ± 2.56 and 6.47 ± 3.22, work load of right is higher than the left; static load analysis indicated the value of RMS(20%MVC) before work was higher than that value after work, the increase of right CES and UT RMS(20%MVC) was more; the largest 20%MVE of bilateral CES occurred at 20th time window, and that of bilateral UT happened at 16th. The work load of female sewing machine operators is sustained "static" load, and work load of right neck-shoulder is higher than left, right neck-shoulder muscle is more fatigable and much serious once fatigued.

State of the Art and Challenges of Radio Spectrum Monitoring in China

NASA Astrophysics Data System (ADS)

Lu, Q. N.; Yang, J. J.; Jin, Z. Y.; Chen, D. Z.; Huang, M.

2017-10-01

This paper provides an overview of radio spectrum monitoring in China. First, research background, the motivation is described and then train of thought, the prototype system, and the accomplishments are presented. Current radio spectrum monitoring systems are man-machine communication systems, which are unable to detect and process the radio interference automatically. In order to realize intelligent radio monitoring and spectrum management, we proposed an Internet of Things-based spectrum sensing approach using information system architecture and implemented a pilot program; then some very interesting results were obtained.

Development of plant condition measurement - The Jimah Model

NASA Astrophysics Data System (ADS)

Evans, Roy F.; Syuhaimi, Mohd; Mazli, Mohammad; Kamarudin, Nurliyana; Maniza Othman, Faiz

2012-05-01

The Jimah Model is an information management model. The model has been designed to facilitate analysis of machine condition by integrating diagnostic data with quantitative and qualitative information. The model treats data as a single strand of information - metaphorically a 'genome' of data. The 'Genome' is structured to be representative of plant function and identifies the condition of selected components (or genes) in each machine. To date in industry, computer aided work processes used with traditional industrial practices, have been unable to consistently deliver a standard of information suitable for holistic evaluation of machine condition and change. Significantly the reengineered site strategies necessary for implementation of this "data genome concept" have resulted in enhanced knowledge and management of plant condition. In large plant with high initial equipment cost and subsequent high maintenance costs, accurate measurement of major component condition becomes central to whole of life management and replacement decisions. A case study following implementation of the model at a major power station site in Malaysia (Jimah) shows that modeling of plant condition and wear (in real time) can be made a practical reality.

Occupational Noise Reduction in CNC Striping Process

NASA Astrophysics Data System (ADS)

Mahmad Khairai, Kamarulzaman; Shamime Salleh, Nurul; Razlan Yusoff, Ahmad

2018-03-01

Occupational noise hearing loss with high level exposure is common occupational hazards. In CNC striping process, employee that exposed to high noise level for a long time as 8-hour contributes to hearing loss, create physical and psychological stress that reduce productivity. In this paper, CNC stripping process with high level noises are measured and reduced to the permissible noise exposure. First condition is all machines shutting down and second condition when all CNC machine under operations. For both conditions, noise exposures were measured to evaluate the noise problems and sources. After improvement made, the noise exposures were measured to evaluate the effectiveness of reduction. The initial average noise level at the first condition is 95.797 dB (A). After the pneumatic system with leakage was solved, the noise reduced to 55.517 dB (A). The average noise level at the second condition is 109.340 dB (A). After six machines were gathered at one area and cover that area with plastic curtain, the noise reduced to 95.209 dB (A). In conclusion, the noise level exposure in CNC striping machine is high and exceed the permissible noise exposure can be reduced to acceptable levels. The reduction of noise level in CNC striping processes enhanced productivity in the industry.

Virtual Machine Language

NASA Technical Reports Server (NTRS)

Grasso, Christopher; Page, Dennis; O'Reilly, Taifun; Fteichert, Ralph; Lock, Patricia; Lin, Imin; Naviaux, Keith; Sisino, John

2005-01-01

Virtual Machine Language (VML) is a mission-independent, reusable software system for programming for spacecraft operations. Features of VML include a rich set of data types, named functions, parameters, IF and WHILE control structures, polymorphism, and on-the-fly creation of spacecraft commands from calculated values. Spacecraft functions can be abstracted into named blocks that reside in files aboard the spacecraft. These named blocks accept parameters and execute in a repeatable fashion. The sizes of uplink products are minimized by the ability to call blocks that implement most of the command steps. This block approach also enables some autonomous operations aboard the spacecraft, such as aerobraking, telemetry conditional monitoring, and anomaly response, without developing autonomous flight software. Operators on the ground write blocks and command sequences in a concise, high-level, human-readable programming language (also called VML ). A compiler translates the human-readable blocks and command sequences into binary files (the operations products). The flight portion of VML interprets the uplinked binary files. The ground subsystem of VML also includes an interactive sequence- execution tool hosted on workstations, which runs sequences at several thousand times real-time speed, affords debugging, and generates reports. This tool enables iterative development of blocks and sequences within times of the order of seconds.

Machine Learning Algorithms for prediction of regions of high Reynolds Averaged Navier Stokes Uncertainty

NASA Astrophysics Data System (ADS)

Mishra, Aashwin; Iaccarino, Gianluca

2017-11-01

In spite of their deficiencies, RANS models represent the workhorse for industrial investigations into turbulent flows. In this context, it is essential to provide diagnostic measures to assess the quality of RANS predictions. To this end, the primary step is to identify feature importances amongst massive sets of potentially descriptive and discriminative flow features. This aids the physical interpretability of the resultant discrepancy model and its extensibility to similar problems. Recent investigations have utilized approaches such as Random Forests, Support Vector Machines and the Least Absolute Shrinkage and Selection Operator for feature selection. With examples, we exhibit how such methods may not be suitable for turbulent flow datasets. The underlying rationale, such as the correlation bias and the required conditions for the success of penalized algorithms, are discussed with illustrative examples. Finally, we provide alternate approaches using convex combinations of regularized regression approaches and randomized sub-sampling in combination with feature selection algorithms, to infer model structure from data. This research was supported by the Defense Advanced Research Projects Agency under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo).

Tool wear compensation scheme for DTM

NASA Astrophysics Data System (ADS)

Sandeep, K.; Rao, U. S.; Balasubramaniam, R.

2018-04-01

This paper is aimed to monitor tool wear in diamond turn machining (DTM), assess effects of tool wear on accuracies of the machined component, and develop compensation methodology to enhance size and shape accuracies of a hemispherical cup. In order to find change in the centre and radius of tool with increasing wear of tool, a MATLAB program is used. In practice, x-offsets are readjusted by DTM operator for desired accuracy in the cup and the results of theoretical model show that change in radius and z-offset are insignificant however x-offset is proportional to the tool wear and this is what assumed while resetting tool offset. Since we could not measure the profile of tool; therefore we modeled our program for cup profile data. If we assume no error due to slide and spindle of DTM then any wear in the tool will be reflected in the cup profile. As the cup data contains surface roughness, therefore random noise similar to surface waviness is added. It is observed that surface roughness affects the centre and radius but pattern of shifting of centre with increase in wear of tool remains similar to the ideal condition, i.e. without surface roughness.

Secure Autonomous Automated Scheduling (SAAS). Rev. 1.1

NASA Technical Reports Server (NTRS)

Walke, Jon G.; Dikeman, Larry; Sage, Stephen P.; Miller, Eric M.

2010-01-01

This report describes network-centric operations, where a virtual mission operations center autonomously receives sensor triggers, and schedules space and ground assets using Internet-based technologies and service-oriented architectures. For proof-of-concept purposes, sensor triggers are received from the United States Geological Survey (USGS) to determine targets for space-based sensors. The Surrey Satellite Technology Limited (SSTL) Disaster Monitoring Constellation satellite, the UK-DMC, is used as the space-based sensor. The UK-DMC's availability is determined via machine-to-machine communications using SSTL's mission planning system. Access to/from the UK-DMC for tasking and sensor data is via SSTL's and Universal Space Network's (USN) ground assets. The availability and scheduling of USN's assets can also be performed autonomously via machine-to-machine communications. All communication, both on the ground and between ground and space, uses open Internet standards

The Altar Machine in the Church Mother of Gangi (Palermo, Italy). Interpretation of the past uses, scientific investigation and preservation challenge

PubMed Central

2012-01-01

Background The aim of this work was to study the Altar Machine in the Church Mother of Gangi, a little town near Palermo (Italy) regarding the history, the technical manufacture, the constitutive materials and the state of preservation. The Altar Machine was dated back to the second half of the 18th century; it is constituted by carved and painted wood, a complex system of winch and pulleys allows move various statues and parts of the Machine in accordance with the baroque scenography machineries. Results The observation and survey of the mechanisms allowed formulate hypothesis on a more ancient mode of operation of the Altar Machine. Laboratory analysis revealed the presence of many superimposed layers constituted by several different materials (protein binders, siccative oils, natural terpene resins, shellac, calcium carbonate, gypsum, lead white, brass, zinc white, iron oxides) and different wood species employed for the original and restoration elements of the Machine. This is due to a continuous usage of the object that has got a demo-ethno-anthropological significance. Microclimate monitoring (relative humidity RH and temperature T) put in evidence that most of the data fall outside the tolerance intervals, i.e. the RH and T limits defined by the international standards. In particular, T values were generally high (out of the tolerance range) but they appeared to be quite constant; on the other hand RH values fell almost always inside the tolerance area but they often exhibited dangerous variations. Conclusions The characterization of the constitutive materials provided useful information both to support the dating of the Machine proposed by the inscription and to obtain a base of data for a possible conservation work. The microclimate monitoring put in evidence that the temperature and relative humidity values are not always suitable to correctly preserve the artefact. The careful in situ investigation confirmed an on-going climate induced damage to the Altar Machine that, associated to the deterioration caused by its usage, may have dramatic consequences on this unique and peculiar work of art. The results of this work will have potential implications in the near future regarding a probable conservation project on the Machine. PMID:22616574

Human campylobacteriosis related to the consumption of raw milk sold by vending machines in Italy: Quantitative risk assessment based on official controls over four years.

PubMed

Giacometti, Federica; Bonilauri, Paolo; Amatiste, Simonetta; Arrigoni, Norma; Bianchi, Manila; Losio, Marina Nadia; Bilei, Stefano; Cascone, Giuseppe; Comin, Damiano; Daminelli, Paolo; Decastelli, Lucia; Merialdi, Giuseppe; Mioni, Renzo; Peli, Angelo; Petruzzelli, Annalisa; Tonucci, Franco; Piva, Silvia; Serraino, Andrea

2015-09-01

A quantitative risk assessment (RA) model was developed to describe the risk of campylobacteriosis linked to consumption of raw milk sold in vending machines in Italy. Exposure assessment was based on the official microbiological records of raw milk samples from vending machines monitored by the regional Veterinary Authorities from 2008 to 2011, microbial growth during storage, destruction experiments, consumption frequency of raw milk, serving size, consumption preference and age of consumers. The differential risk considered milk handled under regulation conditions (4°C throughout all phases) and the worst time-temperature field handling conditions detected. Two separate RA models were developed, one for the consumption of boiled milk and the other for the consumption of raw milk, and two different dose-response (D-R) relationships were considered. The RA model predicted no human campylobacteriosis cases per year either in the best (4°C) storage conditions or in the case of thermal abuse in case of boiling raw milk, whereas in case of raw milk consumption the annual estimated campylobacteriosis cases depend on the dose-response relationships used in the model (D-R I or D-R II), the milk time-temperature storage conditions, consumer behaviour and age of consumers, namely young (with two cut-off values of ≤5 or ≤6 years old for the sensitive population) versus adult consumers. The annual estimated cases for young consumers using D-R II for the sensitive population (≤5 years old) ranged between 1013.7/100,000 population and 8110.3/100,000 population and for adult consumers using D-R I between 79.4/100,000 population and 333.1/100,000 population. Quantification of the risks associated with raw milk consumption is necessary from a public health perspective and the proposed RA model represents a useful and flexible tool to perform future RAs based on local consumer habits to support decision-making on safety policies. Further educational programmes for raw milk consumers or potential raw milk consumers are required to encourage consumers to boil milk to reduce the associated risk of illness. Copyright © 2015 Elsevier B.V. All rights reserved.

Automatic fog detection for public safety by using camera images

NASA Astrophysics Data System (ADS)

Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

2017-04-01

Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods to classify the dense fog conditions (i.e., visibility below 250 meters) show promising results (in terms of accuracy and type I and II errors). We are currently extending the approach to images obtained with traffic-monitoring cameras along highways. This is a first step to reach a solution that is closer to an operational artificial intelligence application for automatic fog alarm signaling for public safety.

Virtual reality hardware and graphic display options for brain-machine interfaces

PubMed Central

Marathe, Amar R.; Carey, Holle L.; Taylor, Dawn M.

2009-01-01

Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where able-bodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing. PMID:18006069

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control.

PubMed

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-04-09

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

PubMed Central

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-01-01

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web. PMID:28397776

Hydrocarbon pneumonia

MedlinePlus

... pneumonia is caused by drinking or breathing in gasoline , kerosene , furniture polish , paint thinner, or other oily ... Arterial blood gas monitoring Breathing support, including oxygen, inhalation treatment, breathing tube and ventilator (machine), in severe ...

Lymphangiogram

MedlinePlus

... type of x-ray machine, called a fluoroscope, projects the images on a TV monitor. The provider ... commercial use must be authorized in writing by ADAM Health Solutions. About MedlinePlus Site Map FAQs Customer ...

Machine assisted histogram classification

NASA Astrophysics Data System (ADS)

Benyó, B.; Gaspar, C.; Somogyi, P.

2010-04-01

LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.

Overview of recent trends and developments for BPM systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wendt, M.; /Fermilab

2011-08-01

Beam position monitoring (BPM) systems are the workhorse of beam diagnostics for almost any kind of charged particle accelerator: linear, circular or transport-lines, operating with leptons, hadrons or heavy ions. BPMs are essential for beam commissioning, accelerator fault analysis and trouble shooting, machine optics, as well as lattice measurements, and finally, for accelerator optimization, in order to achieve the ultimate beam quality. This presentation summarizes the efforts of the beam instrumentation community on recent developments and advances on BPM technologies, i.e. BPM pickup monitors and front-end electronics (analog and digital). Principles, examples, and state-of-the-art status on various BPM techniques, servingmore » hadron and heavy ion machines, sync light synchrotron's, as well as electron linacs for FEL or HEP applications are outlined.« less

AUTOMATING ASSET KNOWLEDGE WITH MTCONNECT.

PubMed

Venkatesh, Sid; Ly, Sidney; Manning, Martin; Michaloski, John; Proctor, Fred

2016-01-01

In order to maximize assets, manufacturers should use real-time knowledge garnered from ongoing and continuous collection and evaluation of factory-floor machine status data. In discrete parts manufacturing, factory machine monitoring has been difficult, due primarily to closed, proprietary automation equipment that make integration difficult. Recently, there has been a push in applying the data acquisition concepts of MTConnect to the real-time acquisition of machine status data. MTConnect is an open, free specification aimed at overcoming the "Islands of Automation" dilemma on the shop floor. With automated asset analysis, manufacturers can improve production to become lean, efficient, and effective. The focus of this paper will be on the deployment of MTConnect to collect real-time machine status to automate asset management. In addition, we will leverage the ISO 22400 standard, which defines an asset and quantifies asset performance metrics. In conjunction with these goals, the deployment of MTConnect in a large aerospace manufacturing facility will be studied with emphasis on asset management and understanding the impact of machine Overall Equipment Effectiveness (OEE) on manufacturing.

The coffee-machine bacteriome: biodiversity and colonisation of the wasted coffee tray leach

PubMed Central

Vilanova, Cristina; Iglesias, Alba; Porcar, Manuel

2015-01-01

Microbial communities are ubiquitous in both natural and artificial environments. However, microbial diversity is usually reduced under strong selection pressures, such as those present in habitats rich in recalcitrant or toxic compounds displaying antimicrobial properties. Caffeine is a natural alkaloid present in coffee, tea and soft drinks with well-known antibacterial properties. Here we present the first systematic analysis of coffee machine-associated bacteria. We sampled the coffee waste reservoir of ten different Nespresso machines and conducted a dynamic monitoring of the colonization process in a new machine. Our results reveal the existence of a varied bacterial community in all the machines sampled, and a rapid colonisation process of the coffee leach. The community developed from a pioneering pool of enterobacteria and other opportunistic taxa to a mature but still highly variable microbiome rich in coffee-adapted bacteria. The bacterial communities described here, for the first time, are potential drivers of biotechnologically relevant processes including decaffeination and bioremediation. PMID:26592442

The coffee-machine bacteriome: biodiversity and colonisation of the wasted coffee tray leach.

PubMed

Vilanova, Cristina; Iglesias, Alba; Porcar, Manuel

2015-11-23

Microbial communities are ubiquitous in both natural and artificial environments. However, microbial diversity is usually reduced under strong selection pressures, such as those present in habitats rich in recalcitrant or toxic compounds displaying antimicrobial properties. Caffeine is a natural alkaloid present in coffee, tea and soft drinks with well-known antibacterial properties. Here we present the first systematic analysis of coffee machine-associated bacteria. We sampled the coffee waste reservoir of ten different Nespresso machines and conducted a dynamic monitoring of the colonization process in a new machine. Our results reveal the existence of a varied bacterial community in all the machines sampled, and a rapid colonisation process of the coffee leach. The community developed from a pioneering pool of enterobacteria and other opportunistic taxa to a mature but still highly variable microbiome rich in coffee-adapted bacteria. The bacterial communities described here, for the first time, are potential drivers of biotechnologically relevant processes including decaffeination and bioremediation.

Wear detection by means of wavelet-based acoustic emission analysis

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2015-08-01

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring systems can be build, able to evaluate automatically the surface condition of machine components with sliding surfaces.

Natural frequency identification of smart washer by using adaptive observer

NASA Astrophysics Data System (ADS)

Ito, Hitoshi; Okugawa, Masayuki

2014-04-01

Bolted joints are used in many machines/structures and some of them have been loosened during long time use, and unluckily these bolt loosening may cause a great accident of machines/structures system. These bolted joint, especially in important places, are main object of maintenance inspection. Maintenance inspection with human- involvement is desired to be improved owing to time-consuming, labor-intensive and high-cost. By remote and full automation monitoring of the bolt loosening, constantly monitoring of bolted joint is achieved. In order to detect loosening of bolted joints without human-involvement, applying a structural health monitoring technique and smart structures/materials concept is the key objective. In this study, a new method of bolt loosening detection by adopting a smart washer has been proposed, and the basic detection principle was discussed with numerical analysis about frequency equation of the system, was confirmed experimentally. The smart washer used in this study is in cantilever type with piezoelectric material, which adds the washer the self-sensing and actuation function. The principle used to detect the loosening of the bolts is a method of a bolt loosening detection noted that the natural frequency of a smart washer system is decreasing by the change of the bolt tightening axial tension. The feature of this proposed method is achieving to identify the natural frequency at current condition on demand by adopting the self-sensing and actuation function and system identification algorithm for varying the natural frequency depending the bolt tightening axial tension. A novel bolt loosening detection method by adopting adaptive observer is proposed in this paper. The numerical simulations are performed to verify the possibility of the adaptive observer-based loosening detection. Improvement of the detection accuracy for a bolt loosening is confirmed by adopting initial parameter and variable adaptive gain by numerical simulation.

Ultimate computing. Biomolecular consciousness and nano Technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hameroff, S.R.

1987-01-01

The book advances the premise that the cytoskeleton is the cell's nervous system, the biological controller/computer. If indeed cytoskeletal dynamics in the nanoscale (billionth meter, billionth second) are the texture of intracellular information processing, emerging ''NanoTechnologies'' (scanning tunneling microscopy, Feynman machines, von Neumann replicators, etc.) should enable direct monitoring, decoding and interfacing between biological and technological information devices. This in turn could result in important biomedical applications and perhaps a merger of mind and machine: Ultimate Computing.

Aerospace Mechanisms Symposium (22nd) Held at Hampton, Virginia on 4-6 May 1988.

DTIC Science & Technology

1988-05-06

monitoring is accomplished by a pressure transducer located near the hole drilled through the vessel wall between seals. A lip is machined on the...are presented and a design example involving a machine tool metrology bench is given. Design goals included thousandfold strain attenuation in the...systems such as a metrology bench, etc. These bodies must be supported. Six degrees of freedom must be fixed, but if the base upon which they are