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Sample records for multivariate condition monitoring

  1. Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions

    NASA Astrophysics Data System (ADS)

    Zimroz, Radoslaw; Bartkowiak, Anna

    2013-07-01

    This paper deals with the diagnostics of planetary gearboxes under nonstationary operating conditions. In most diagnostics applications, energy of vibration signals (calculated directly from time series or extracted from spectral representation of signal) is used. Unfortunately energy based features are sensitive to load conditions and it makes diagnostics difficult. In this paper we used energy based 15D data vectors (namely spectral amplitudes of planetary mesh frequency and its harmonics) in order to investigate if it is possible to improve diagnostics efficiency in comparison to previous, one dimensional, approaches proposed for the same problem. Two multivariate methods, Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA), were used as techniques for data analysis. We used these techniques in order to investigate dimensionality of the data and to visualize data in 3D and 2D spaces in order to understand data behavior and assess classification ability. As a case study the data from two planetary gearboxes used in complex mining machines (one in bad condition and the other in good condition) were analyzed. For these two machines more than 2000 15D vectors were acquired. It should be noted that due to non-stationarity of loading conditions, previous diagnostics results obtained using other techniques were moderately good (ca. 80% recognition efficiency); however there is still some need to improve diagnostics classification ability. After application of the proposed approaches it was found that the entire data could be reduced to 2 dimensions whereby data instances became visible and a good discriminant function (characterized by a misclassification rate of .0023, i.e. only 5 erroneous classifications for a total of 2183 instances) could be derived. This paper suggests a novel way for condition monitoring of planetary gearboxes based on multivariate statistics. The emphasis is put on the algebraic and geometric interpretations of the PCA

  2. Bioharness™ Multivariable Monitoring Device: Part. I: Validity

    PubMed Central

    Johnstone, James A.; Ford, Paul A.; Hughes, Gerwyn; Watson, Tim; Garrett, Andrew T.

    2012-01-01

    The Bioharness™ monitoring system may provide physiological information on human performance but there is limited information on its validity. The objective of this study was to assess the validity of all 5 Bioharness™ variables using a laboratory based treadmill protocol. 22 healthy males participated. Heart rate (HR), Breathing Frequency (BF) and Accelerometry (ACC) precision were assessed during a discontinuous incremental (0-12 km·h-1) treadmill protocol. Infra-red skin temperature (ST) was assessed during a 45 min-1 sub-maximal cycle ergometer test, completed twice, with environmental temperature controlled at 20 ± 0.1 °C and 30 ± 0.1 °C. Posture (P) was assessed using a tilt table moved through 160°. Adopted precision of measurement devices were; HR: Polar T31 (Polar Electro), BF: Spirometer (Cortex Metalyser), ACC: Oxygen expenditure (Cortex Metalyser), ST: Skin thermistors (Grant Instruments), P:Goniometer (Leighton Flexometer). Strong relationships (r = .89 to .99, p < 0.01) were reported for HR, BF, ACC and P. Limits of agreement identified differences in HR (-3.05 ± 32.20 b·min-1), BF (-3.46 ± 43.70 br·min-1) and P (0.20 ± 2.62°). ST established a moderate relationships (-0.61 ± 1.98 °C; r = 0.76, p < 0.01). Higher velocities on the treadmill decreased the precision of measurement, especially HR and BF. Global results suggest that the BioharressTM is a valid multivariable monitoring device within the laboratory environment. Key pointsDifferent levels of precision exist for each variable in the Bioharness™ (Version 1) multi-variable monitoring deviceAccelerometry and posture variables presented the most precise dataData from the heart rate and breathing frequency variable decrease in precision at velocities ≥ 10 km·h-1Clear understanding of the limitations of new applied monitoring technology is required before it is used by the exercise scientist PMID:24149346

  3. Bioharness™ Multivariable Monitoring Device: Part. II: Reliability

    PubMed Central

    Johnstone, James A.; Ford, Paul A.; Hughes, Gerwyn; Watson, Tim; Garrett, Andrew T.

    2012-01-01

    The Bioharness™ monitoring system may provide physiological information on human performance but the reliability of this data is fundamental for confidence in the equipment being used. The objective of this study was to assess the reliability of each of the 5 Bioharness™ variables using a treadmill based protocol. 10 healthy males participated. A between and within subject design to assess the reliability of Heart rate (HR), Breathing Frequency (BF), Accelerometry (ACC) and Infra-red skin temperature (ST) was completed via a repeated, discontinuous, incremental treadmill protocol. Posture (P) was assessed by a tilt table, moved through 160°. Between subject data reported low Coefficient of Variation (CV) and strong correlations(r) for ACC and P (CV< 7.6; r = 0.99, p < 0.01). In contrast, HR and BF (CV~19.4; r~0.70, p < 0.01) and ST (CV 3.7; r = 0.61, p < 0.01), present more variable data. Intra and inter device data presented strong relationships (r > 0.89, p < 0.01) and low CV (<10.1) for HR, ACC, P and ST. BF produced weaker relationships (r < 0.72) and higher CV (<17.4). In comparison to the other variables BF variable consistently presents less reliability. Global results suggest that the Bioharness™ is a reliable multivariable monitoring device during laboratory testing within the limits presented. Key pointsHeart rate and breathing frequency data increased in variance at higher velocities (i.e. ≥ 10 km.h-1)In comparison to the between subject testing, the intra and inter reliability presented good reliability in data suggesting placement or position of device relative to performer could be important for data collectionUnderstanding a devices variability in measurement is important before it can be used within an exercise testing or monitoring setting PMID:24149347

  4. Monitoring Quality of Biotherapeutic Products Using Multivariate Data Analysis.

    PubMed

    Rathore, Anurag S; Pathak, Mili; Jain, Renu; Jadaun, Gaurav Pratap Singh

    2016-07-01

    Monitoring the quality of pharmaceutical products is a global challenge, heightened by the implications of letting subquality drugs come to the market on public safety. Regulatory agencies do their due diligence at the time of approval as per their prescribed regulations. However, product quality needs to be monitored post-approval as well to ensure patient safety throughout the product life cycle. This is particularly complicated for biotechnology-based therapeutics where seemingly minor changes in process and/or raw material attributes have been shown to have a significant effect on clinical safety and efficacy of the product. This article provides a perspective on the topic of monitoring the quality of biotech therapeutics. In the backdrop of challenges faced by the regulatory agencies, the potential use of multivariate data analysis as a tool for effective monitoring has been proposed. Case studies using data from several insulin biosimilars have been used to illustrate the key concepts. PMID:27044370

  5. A direct-gradient multivariate index of biotic condition

    USGS Publications Warehouse

    Miranda, Leandro E.; Aycock, J.N.; Killgore, K. J.

    2012-01-01

    Multimetric indexes constructed by summing metric scores have been criticized despite many of their merits. A leading criticism is the potential for investigator bias involved in metric selection and scoring. Often there is a large number of competing metrics equally well correlated with environmental stressors, requiring a judgment call by the investigator to select the most suitable metrics to include in the index and how to score them. Data-driven procedures for multimetric index formulation published during the last decade have reduced this limitation, yet apprehension remains. Multivariate approaches that select metrics with statistical algorithms may reduce the level of investigator bias and alleviate a weakness of multimetric indexes. We investigated the suitability of a direct-gradient multivariate procedure to derive an index of biotic condition for fish assemblages in oxbow lakes in the Lower Mississippi Alluvial Valley. Although this multivariate procedure also requires that the investigator identify a set of suitable metrics potentially associated with a set of environmental stressors, it is different from multimetric procedures because it limits investigator judgment in selecting a subset of biotic metrics to include in the index and because it produces metric weights suitable for computation of index scores. The procedure, applied to a sample of 35 competing biotic metrics measured at 50 oxbow lakes distributed over a wide geographical region in the Lower Mississippi Alluvial Valley, selected 11 metrics that adequately indexed the biotic condition of five test lakes. Because the multivariate index includes only metrics that explain the maximum variability in the stressor variables rather than a balanced set of metrics chosen to reflect various fish assemblage attributes, it is fundamentally different from multimetric indexes of biotic integrity with advantages and disadvantages. As such, it provides an alternative to multimetric procedures.

  6. Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

    PubMed Central

    Sabit, Hakilo; Al-Anbuky, Adnan

    2014-01-01

    Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

  7. Multivariate spatial condition mapping using subtractive fuzzy cluster means.

    PubMed

    Sabit, Hakilo; Al-Anbuky, Adnan

    2014-01-01

    Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining. PMID:25313495

  8. Multivariate monitoring of wastewater treatment processes in pulp and paper industry

    SciTech Connect

    Mujunen, S.

    1999-05-19

    The objective of this thesis was to study factors affecting the treatment efficiency in pulp and paper mills activated sludge plants. Another objective was to develop a process monitoring system based on statistical multivariate methods. A short review of activated sludge plants within pulp and paper industry and basic theory of multivariate data analysis is given at the beginning. The effects of pulp and paper mill effluents and their treatment with the activated sludge process are discussed in the first experimental part. The process and control parameters of the process are studied in the next chapters. Effluent quality and sludge settling ability are studied with PLS (Partial Least Squares) models. The main process parameters affecting each effluent quality parameter are selected and the parameters were modeled separately. Lack of relevant information is discussed and a new monitoring parameter, ATP pool, is introduced. The objective of this luminometric assay study is to provide an easy to measure quantity that would quickly give a sufficiently accurate estimate of the amount of biologically active sludge. The experimental data determined in laboratory and mill conditions are presented. Finally, the principal idea of a monitoring system is introduced and evaluated by using experimental data measured at three pulp and paper mills. The goal is to develop a process monitoring system, that would detect the approaching process drift, e.g., bulking state, on its early stage and by this manner enable right control actions in time.

  9. Rocket engine condition monitoring system

    SciTech Connect

    Hagar, S.K.; Alcock, J.F.

    1989-01-01

    It is expected that the Rocket Engine Condition Monitoring System (RECMS) program will define engine monitoring technologies and an integration approach which can be applied to engine development in support of advanced launch system objectives. The RECMS program approaches engine monitoring as a system which is fully integrated with the engine controller, vehicle monitoring system, and ground processing systems to ensure mission success in addition to engine reliability. The system components are monitored through health and performance sensors; they are analyzed with the diagnostic and prognostic algorithms and demonstrated by system testing with hardware from other advanced development programs.

  10. Oil Analysis and Condition Monitoring

    NASA Astrophysics Data System (ADS)

    Toms, A.; Toms, L.

    Lubricants are essential and expensive components of machine systems needing sampling, analysis and monitoring. Monitoring can be either performance testing or oil condition monitoring. Knowledge of the system's critical failure modes is essential for cost-effective oil and machinery monitoring. Contamination occurs by water, fuel, glycol, dirt, wrong oil, metal particulate, soot, oil degradation and additive depletion. Oil test methods include in situ or laboratory FT-IR, electronic particle counting, elemental metal measurement, X-ray fluorescence, viscosity, gas chromatography, water determination and RULER®. Condition monitoring data must be managed by storage, analysis and interpretation. Status levels must be established from the database and reported upon for individual and sequential runs of samples as condition indicators.

  11. Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

    PubMed

    Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

    2014-07-15

    This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. PMID:24594186

  12. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis.

    PubMed

    Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

    2012-07-01

    Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission (P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission. PMID:21744100

  13. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

    NASA Astrophysics Data System (ADS)

    Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

    2012-07-01

    Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

  14. A Computer Interview for Multivariate Monitoring of Psychiatric Outcome.

    ERIC Educational Resources Information Center

    Stevenson, John F.; And Others

    Application of computer technology to psychiatric outcome measurement offers the promise of coping with increasing demands for extensive patient interviews repeated longitudinally. Described is the development of a cost-effective multi-dimensional tracking device to monitor psychiatric functioning, building on a previous local computer interview…

  15. The Multi-Isotope Process Monitor: Multivariate Analysis of Gamma Spectra

    SciTech Connect

    Orton, Christopher R.; Rutherford, Crystal E.; Fraga, Carlos G.; Schwantes, Jon M.

    2011-10-30

    The International Atomic Energy Agency (IAEA) has established international safeguards standards for fissionable material at spent fuel reprocessing plants to ensure that significant quantities of nuclear material are not diverted from these facilities. Currently, methods to verify material control and accountancy (MC&A) at these facilities require time-consuming and resource-intensive destructive assay (DA). The time delay between sampling and subsequent DA provides a potential opportunity to divert the material out of the appropriate chemical stream. Leveraging new on-line nondestructive assay (NDA) techniques in conjunction with the traditional and highly precise DA methods may provide a more timely, cost-effective and resource efficient means for MC&A verification at such facilities. Pacific Northwest National Laboratory (PNNL) is developing on-line NDA process monitoring technologies, including the Multi-Isotope Process (MIP) Monitor. The MIP Monitor uses gamma spectroscopy and pattern recognition software to identify off-normal conditions in process streams. Recent efforts have been made to explore the basic limits of using multivariate analysis techniques on gamma-ray spectra. This paper will provide an overview of the methods and report our on-going efforts to develop and demonstrate the technology.

  16. Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

    PubMed

    Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

    2016-05-15

    Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. PMID:26883066

  17. Monitoring of an industrial process by multivariate control charts based on principal component analysis.

    PubMed

    Marengo, Emilio; Gennaro, Maria Carla; Gianotti, Valentina; Robotti, Elisa

    2003-01-01

    The control and monitoring of an industrial process is performed in this paper by the multivariate control charts. The process analysed consists of the bottling of the entire production of 1999 of the sparkling wine "Asti Spumante". This process is characterised by a great number of variables that can be treated with multivariate techniques. The monitoring of the process performed with classical Shewhart charts is very dangerous because they do not take into account the presence of functional relationships between the variables. The industrial process was firstly analysed by multivariate control charts based on Principal Component Analysis. This approach allowed the identification of problems in the process and of their causes. Successively, the SMART Charts (Simultaneous Scores Monitoring And Residual Tracking) were built in order to study the process in its whole. In spite of the successful identification of the presence of problems in the monitored process, the Smart chart did not allow an easy identification of the special causes of variation which casued the problems themselves. PMID:12911145

  18. Wind Turbine Drivetrain Condition Monitoring - An Overview

    SciTech Connect

    Sheng, S; Veers, P.

    2011-10-01

    This paper provides an overview of wind turbine drivetrain condition monitoring based on presentations from a condition monitoring workshop organized by the National Renewable Energy Laboratory in 2009 and on additional references.

  19. Use of multivariate calibration models based on UV-Vis spectra for seawater quality monitoring in Tianjin Bohai Bay, China.

    PubMed

    Liu, Xianhua; Wang, Lili

    2015-01-01

    A series of ultraviolet-visible (UV-Vis) spectra from seawater samples collected from sites along the coastline of Tianjin Bohai Bay in China were subjected to multivariate partial least squares (PLS) regression analysis. Calibration models were developed for monitoring chemical oxygen demand (COD) and concentrations of total organic carbon (TOC). Three different PLS models were developed using the spectra from raw samples (Model-1), diluted samples (Model-2), and diluted and raw samples combined (Model-3). Experimental results showed that: (i) possible nonlinearities in the signal concentration relationships were well accounted for by the multivariate PLS model; (ii) the predicted values of COD and TOC fit the analytical values well; the high correlation coefficients and small root mean squared error of cross-validation (RMSECV) showed that this method can be used for seawater quality monitoring; and (iii) compared with Model-1 and Model-2, Model-3 had the highest coefficient of determination (R2) and the lowest number of latent variables. This latter finding suggests that only large data sets that include data representing different combinations of conditions (i.e., various seawater matrices) will produce stable site-specific regressions. The results of this study illustrate the effectiveness of the proposed method and its potential for use as a seawater quality monitoring technique. PMID:26442484

  20. Effect of altered sensory conditions on multivariate descriptors of human postural sway

    NASA Technical Reports Server (NTRS)

    Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

    1998-01-01

    Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

  1. Sustainable microbial water quality monitoring programme design using phage-lysis and multivariate techniques.

    PubMed

    Nnane, Daniel Ekane

    2011-11-15

    Contamination of surface waters is a pervasive threat to human health, hence, the need to better understand the sources and spatio-temporal variations of contaminants within river catchments. River catchment managers are required to sustainably monitor and manage the quality of surface waters. Catchment managers therefore need cost-effective low-cost long-term sustainable water quality monitoring and management designs to proactively protect public health and aquatic ecosystems. Multivariate and phage-lysis techniques were used to investigate spatio-temporal variations of water quality, main polluting chemophysical and microbial parameters, faecal micro-organisms sources, and to establish 'sentry' sampling sites in the Ouse River catchment, southeast England, UK. 350 river water samples were analysed for fourteen chemophysical and microbial water quality parameters in conjunction with the novel human-specific phages of Bacteroides GB-124 (Bacteroides GB-124). Annual, autumn, spring, summer, and winter principal components (PCs) explained approximately 54%, 75%, 62%, 48%, and 60%, respectively, of the total variance present in the datasets. Significant loadings of Escherichia coli, intestinal enterococci, turbidity, and human-specific Bacteroides GB-124 were observed in all datasets. Cluster analysis successfully grouped sampling sites into five clusters. Importantly, multivariate and phage-lysis techniques were useful in determining the sources and spatial extent of water contamination in the catchment. Though human faecal contamination was significant during dry periods, the main source of contamination was non-human. Bacteroides GB-124 could potentially be used for catchment routine microbial water quality monitoring. For a cost-effective low-cost long-term sustainable water quality monitoring design, E. coli or intestinal enterococci, turbidity, and Bacteroides GB-124 should be monitored all-year round in this river catchment. PMID:21962927

  2. Plant Condition Remote Monitoring Technique

    NASA Technical Reports Server (NTRS)

    Fotedar, L. K.; Krishen, K.

    1996-01-01

    This paper summarizes the results of a radiation transfer study conducted on houseplants using controlled environmental conditions. These conditions included: (1) air and soil temperature; (2) incident and reflected radiation; and (3) soil moisture. The reflectance, transmittance, and emittance measurements were conducted in six spectral bands: microwave, red, yellow, green, violet and infrared, over a period of three years. Measurements were taken on both healthy and diseased plants. The data was collected on plants under various conditions which included: variation in plant bio-mass, diurnal variation, changes in plant pathological conditions (including changes in water content), different plant types, various disease types, and incident light wavelength or color. Analysis of this data was performed to yield an algorithm for plant disease from the remotely sensed data.

  3. A multivariate conditional model for streamflow prediction and spatial precipitation refinement

    NASA Astrophysics Data System (ADS)

    Liu, Zhiyong; Zhou, Ping; Chen, Xiuzhi; Guan, Yinghui

    2015-10-01

    The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile-copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine-based model is compared with that of traditional data-driven models such as the support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

  4. Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

    NASA Technical Reports Server (NTRS)

    Smith, M. E.; Gevins, A.; Brown, H.; Karnik, A.; Du, R.

    2001-01-01

    Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

  5. Optical Spectroscopy and Multivariate Analysis for Biodosimetry and Monitoring of Radiation Injury to the Skin

    SciTech Connect

    Levitskaia, Tatiana G.; Bryan, Samuel A.; Creim, Jeffrey A.; Curry, Terry L.; Luders, Teresa; Thrall, Karla D.; Peterson, James M.

    2012-08-01

    In the event of an intentional or accidental release of ionizing radiation in a densely populated area, timely assessment and triage of the general population for the radiation exposure is critical. In particular, a significant number of the victims may sustain cutaneous radiation injury, which increases the mortality and worsens the overall prognosis of the victims suffered from combined thermal/mechanical and radiation trauma. Diagnosis of the cutaneous radiation injury is challenging, and established methods largely rely on visual manifestations, presence of the skin contamination, and a high degree of recall by the victim. Availability of a high throughput non-invasive in vivo biodosimetry tool for assessment of the radiation exposure of the skin is of particular importance for the timely diagnosis of the cutaneous injury. In the reported investigation, we have tested the potential of an optical reflectance spectroscopy for the evaluation of the radiation injury to the skin. This is technically attractive because optical spectroscopy relies on well-established and routinely used for various applications instrumentation, one example being pulse oximetry which uses selected wavelengths for the quantification of the blood oxygenation. Our method relies on a broad spectral region ranging from the locally absorbed, shallow-penetrating ultraviolet and visible (250 to 800 nm) to more deeply penetrating near-Infrared (800 – 1600 nm) light for the monitoring of multiple physiological changes in the skin upon irradiation. Chemometrics is a multivariate methodology that allows the information from entire spectral region to be used to generate predictive regression models. In this report we demonstrate that simple spectroscopic method, such as the optical reflectance spectroscopy, in combination with multivariate data analysis, offers the promise of rapid and non-invasive in vivo diagnosis and monitoring of the cutaneous radiation exposure, and is able accurately predict

  6. Multivariate, non-linear trend analysis of heterogeneous water quality monitoring data

    NASA Astrophysics Data System (ADS)

    Lischeid, Gunnar; Kalettka, Thomas; Steidl, Jörg; Merz, Christoph; Lehr, Christian

    2014-05-01

    Comprehensive water quality monitoring is considered a necessary prerequisite for sound water resources management and a valuable source for science. In practice, however, use of large monitoring data sets is often limited due to heterogeneous data sources, spatially and temporally variable monitoring schemes, non-equidistant sampling, large natural variability, and, last but not least, by the sheer size of the data sets that makes identification of unexpected peculiarities a tedious task. As a consequence, any initiation of gradual long-term system shifts can hardly be detected, especially as long as it is restricted to a small fraction of sampling sites. In addition, trends might be limited to a rather small subset of sampling sites or to certain periods of time and might thus escape attention. Usually, numerous solutes are monitored in parallel, but trend analyses are performed for each solute separately. However, in water quality samples trends are hardly restricted to single solutes, but affect various solutes synchronously in a characteristic way. Thus performing joint multivariate trend analyses would not only save effort and time, but would yield more robust assessments of system shifts. We present a non-linear multivariate data visualization approach that allows a rapid assessment of non-linear, possibly local trends and unexpected behaviour in large water quality monitoring data sets. It consists of a combination of Self-Organizing Maps and Sammon's Mapping (SOM-SM). The approach was applied to a data set of 2900 water samples, each comprising 13 solutes, compiled from various monitoring programs in the Federal State of Brandenburg (Germany). In total, 128 stream water, groundwater and small pond sites had been sampled between 1994 and 2012 at different and irregular time intervals. The SOM-SM product is a graph where every sample is represented by a symbol. Location of the symbols in the graph is optimized such that the distance between any two symbols

  7. Bayesian Analysis of the Conditional Correlation Between Stock Index Returns with Multivariate Stochastic Volatility Models

    NASA Astrophysics Data System (ADS)

    Pajor, A.

    2006-11-01

    In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility (SV) models to describe the conditional correlations between stock index returns. We consider four tri-variate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example we study tri-variate volatility models for the daily log returns on the WIG, S&P 500, and FTSE 100 indexes. In order to formally compare the relative explanatory power of SV specifications we use the Bayesian principles of comparing statistic models. Our results are based on the Bayes factors and implemented through Markov Chain Monte Carlo techniques. The results indicate that the most adequate specifications are those that allow for time-varying conditional correlations and that have as many latent processes as there are conditional variances and covariances. The empirical results clearly show that the data strongly reject the assumption of constant conditional correlations.

  8. Web-based machine tool condition monitoring

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Morteza; Victory, J. L.

    2000-12-01

    This paper looks at the advantages of using the Internet, as the basis for the implementation of low-cost condition monitoring systems, in the manufacturing industry. A model based condition monitoring system, is presented where a number of machining stations dispersed at different physical locations can be inspected via Internet access and the signals from the process analyzed in a dedicated condition monitoring center. Incentive for the new approach to the system health monitoring, logging and surveillance are presented. These extend into advantages of using model-based techniques and the need for an appropriate mathematical model of the machine tool. Finally, the data acquisition and communication system to be used in this application for Internet access will be explained.

  9. Water O-H stretching Raman signature for strong acid monitoring via multivariate analysis.

    PubMed

    Casella, Amanda J; Levitskaia, Tatiana G; Peterson, James M; Bryan, Samuel A

    2013-04-16

    A distinct need exists for real time information on an acid concentration of industrial aqueous streams. Acid strength affects efficiency and selectivity of many separation processes, including nuclear fuel reprocessing. Despite the seeming simplicity of the problem, no practical solution has been offered yet, particularly for the large-scale schemes involving toxic streams such as highly radioactive nuclear wastes. The classic potentiometric technique is not amiable for online measurements due to the requirements of frequent calibration/maintenance and poor long-term stability in aggressive chemical and radiation environments. Therefore, an alternative analytical method is needed. In this work, the potential of using Raman spectroscopic measurements for online monitoring of strong acid concentration in solutions relevant to dissolved used nuclear fuel was investigated. The Raman water signature was monitored for solution systems containing nitric and hydrochloric acids and their sodium salts of systematically varied composition, ionic strength, and temperature. The trivalent neodymium ion simulated the presence of multivalent f metals. The gaussian deconvolution analysis was used to interpret observed effects of the solution nature on the Raman water O-H stretching spectrum. The generated Raman spectroscopic database was used to develop predictive multivariate regression models for the quantification of the acid and other solution components, as well as selected physicochemical properties. This method was validated using independent experiments conducted in a flow solvent extraction system. PMID:23472939

  10. Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

    PubMed Central

    Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

    2014-01-01

    Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively; and Cluster Analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments and Vaqueros and La Caldera Rivers were the most similar. Canonical Correlation Analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and Principal Component Analysis allowed finding relationships among the 9 measured variables in all aquatic environments. Variable’s loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros Rivers were influenced by recreational activities. Discriminant Analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

  11. Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach.

    PubMed

    Gutiérrez-Cacciabue, Dolores; Teich, Ingrid; Poma, Hugo Ramiro; Cruz, Mercedes Cecilia; Balzarini, Mónica; Rajal, Verónica Beatriz

    2014-12-01

    Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable's loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved. PMID:25190636

  12. Oxidation management of white wines using cyclic voltammetry and multivariate process monitoring.

    PubMed

    Martins, Rui C; Oliveira, Raquel; Bento, Fatima; Geraldo, Dulce; Lopes, Vitor V; Guedes de Pinho, Paula; Oliveira, Carla M; Silva Ferreira, Antonio C

    2008-12-24

    The development of a fingerprinting strategy capable to evaluate the "oxidation status" of white wines based on cyclic voltammetry is proposed here. It is known that the levels of specific antioxidants and redox mechanisms may be evaluated by cyclic voltammetry. This electrochemical technique was applied on two sets of samples. One group was composed of normal aged white wines and a second group obtained from a white wine forced aging protocol with different oxygen, SO(2), pH, and temperature regimens. A study of antioxidant additions, namely ascorbic acid, was also made in order to establish a statistical link between voltammogram fingerprints and chemical antioxidant substances. It was observed that the oxidation curve presented typical features, which enables sample discrimination according to age, oxygen consumption, and antioxidant additions. In fact, it was possible to place the results into four significant orthogonal directions, compressing 99.8% of nonrandom features. Attempts were made to make voltammogram fingerprinting a tool for monitoring oxidation management. For this purpose, a supervised multivariate control chart was developed using a control sample as reference. When white wines are plotted onto the chart, it is possible to monitor the oxidation status and to diagnose the effects of oxygen regimes and antioxidant activity. Finally, quantification of substances implicated in the oxidation process as reagents (antioxidants) and products (off-flavors) was tried using a supervised algorithmic the partial least square regression analysis. Good correlations (r > 0.93) were observed for ascorbic acid, Folin-Ciocalteu index, total SO(2), methional, and phenylacetaldehyde. These results show that cyclic voltammetry fingerprinting can be used to monitor and diagnose the effects of wine oxidation. PMID:19053361

  13. Infrared thermography for condition monitoring - A review

    NASA Astrophysics Data System (ADS)

    Bagavathiappan, S.; Lahiri, B. B.; Saravanan, T.; Philip, John; Jayakumar, T.

    2013-09-01

    Temperature is one of the most common indicators of the structural health of equipment and components. Faulty machineries, corroded electrical connections, damaged material components, etc., can cause abnormal temperature distribution. By now, infrared thermography (IRT) has become a matured and widely accepted condition monitoring tool where the temperature is measured in real time in a non-contact manner. IRT enables early detection of equipment flaws and faulty industrial processes under operating condition thereby, reducing system down time, catastrophic breakdown and maintenance cost. Last three decades witnessed a steady growth in the use of IRT as a condition monitoring technique in civil structures, electrical installations, machineries and equipment, material deformation under various loading conditions, corrosion damages and welding processes. IRT has also found its application in nuclear, aerospace, food, paper, wood and plastic industries. With the advent of newer generations of infrared camera, IRT is becoming a more accurate, reliable and cost effective technique. This review focuses on the advances of IRT as a non-contact and non-invasive condition monitoring tool for machineries, equipment and processes. Various conditions monitoring applications are discussed in details, along with some basics of IRT, experimental procedures and data analysis techniques. Sufficient background information is also provided for the beginners and non-experts for easy understanding of the subject.

  14. Generation of multivariate near shore extreme wave conditions based on an extreme value copula for offshore boundary conditions.

    NASA Astrophysics Data System (ADS)

    Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris

    2013-04-01

    Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be

  15. A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

    ERIC Educational Resources Information Center

    Ngan, Chun-Kit

    2013-01-01

    Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

  16. Assessing a quick monitoring method using rocky intertidal communities as a bioindicator: a multivariate approach in Algeciras Bay.

    PubMed

    Guerra-García, J M; Maestre, M J; González, A R; García-Gómez, J C

    2006-05-01

    A multivariate approach was used to test the value of intertidal communities as a bioindicator of environmental conditions at Algeciras Bay, southern Spain. The study area is located in the Strait of Gibraltar and it is subjected to a variety of anthropic impacts. Eight localities (5 inside and 3 outside the bay) were selected, and four transects were undertaken in each locality to characterise the fauna and flora. The spatial distribution of the intertidal species reflected the physico-chemical conditions of Algeciras Bay. The stations located outside the bay, characterised by high hydrodynamism and dissolved oxygen and low sedimentation and turbidity, had a higher diversity and species richness than the inner stations. According to the BIO-ENV procedure and CCA, water turbidity was the factor which best correlated with the intertidal assemblages. SIMPER showed that the molluscs Chtamalus stellatus, Mytilus cf edulis, Littorina neritoides and Balanus perforatus, and the algae Gelidium pusillum, Corallina elongata, Asparagopsis armata, Colpomenia sinuosa and Fucus spiralis were the species that most contributed to the dissimilarity between internal and external sites. The present study, based on the spatial distribution of intertidal taxa, yielded similar results to those previously obtained in the area with costly physico-chemical analysis based on complex matrices of subtidal epifaunal communities. Consequently, the intertidal sampling method proposed in this study is presented here as a quick, effective alternative strategy, and can be useful in environmental monitoring programs, since these communities are easily accessible and amenable to sample, and the sessile nature of the majority of the species makes future, long-term monitoring relatively simple. PMID:16779601

  17. Electrical condition monitoring method for polymers

    DOEpatents

    Watkins, Jr. Kenneth S.; Morris, Shelby J.; Masakowski, Daniel D.; Wong, Ching Ping; Luo, Shijian

    2010-02-16

    An electrical condition monitoring method utilizes measurement of electrical resistivity of a conductive composite degradation sensor to monitor environmentally induced degradation of a polymeric product such as insulated wire and cable. The degradation sensor comprises a polymeric matrix and conductive filler. The polymeric matrix may be a polymer used in the product, or it may be a polymer with degradation properties similar to that of a polymer used in the product. The method comprises a means for communicating the resistivity to a measuring instrument and a means to correlate resistivity of the degradation sensor with environmentally induced degradation of the product.

  18. Reusable rocket engine turbopump condition monitoring

    NASA Technical Reports Server (NTRS)

    Hampson, M. E.; Barkhoudarian, S.

    1985-01-01

    Significant improvements in engine readiness with attendant reductions in maintenance costs and turnaround times can be achieved with an engine condition monitoring system (CMS). The CMS provides real time health status of critical engine components, without disassembly, through component monitoring with advanced sensor technologies. Three technologies were selected to monitor the rotor bearings and turbine blades: the isotope wear detector and fiber optic deflectometer (bearings), and the fiber optic pyrometer (blades). Signal processing algorithms were evaluated and ranked for their utility in providing useful component health data to unskilled maintenance personnel. Design modifications to current configuration Space Shuttle Main Engine (SSME) high pressure turbopumps and the MK48-F turbopump were developed to incorporate the sensors.

  19. Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring

    SciTech Connect

    Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.; Schwantes, Jon M.

    2014-01-01

    The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic used fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this

  20. Condition Monitoring of the SSE Generation Fleet

    NASA Astrophysics Data System (ADS)

    Twiddle, J.; Muthuraman, S.; Connolly, N.

    2012-05-01

    SSE (previously known as Scottish and Southern Energy) operates a diverse portfolio of generation plant, including coal, gas and renewable plant with a total generation capacity of 11,375MW (Sept 2011). In recent years a group of specialists dedicated to providing condition monitoring services has been established at the Equipment Performance Centre (EPC) based at Knottingley, West Yorkshire. We aim to illustrate the role of the EPC and the methods used for monitoring the generation fleet with the objective of maintaining asset integrity, reducing risk of plant failure and unplanned outages and describe the challenges which have been overcome in establishing the EPC. This paper describes methods including vibration and process data analysis, model-based techniques and on-site testing used for monitoring of generation plant, including gas turbines, steam turbines, generators and steam raising plant. These condition monitoring processes utilise available data, adding value to the business, by bringing services in-house and capturing knowledge of plant operation for the benefit of the whole fleet.

  1. A Resilient Condition Assessment Monitoring System

    SciTech Connect

    Humberto Garcia; Wen-Chiao Lin; Semyon M. Meerkov

    2012-08-01

    An architecture and supporting methods are presented for the implementation of a resilient condition assessment monitoring system that can adaptively accommodate both cyber and physical anomalies to a monitored system under observation. In particular, the architecture includes three layers: information, assessment, and sensor selection. The information layer estimates probability distributions of process variables based on sensor measurements and assessments of the quality of sensor data. Based on these estimates, the assessment layer then employs probabilistic reasoning methods to assess the plant health. The sensor selection layer selects sensors so that assessments of the plant condition can be made within desired time periods. Resilient features of the developed system are then illustrated by simulations of a simplified power plant model, where a large portion of the sensors are under attack.

  2. Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate /n-Dodecane/Nitric Acid Solvent

    SciTech Connect

    Levitskaia, Tatiana G.; Peterson, James M.; Campbell, Emily L.; Casella, Amanda J.; Peterman, Dean; Bryan, Samuel A.

    2013-11-05

    In liquid-liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness and frequent solvent analysis is warranted. Our research explores feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutyl phosphoric acid (HDBP) was assessed. Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to the high dose external gamma irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.

  3. Fourier Transform Infrared Spectroscopy and Multivariate Analysis for Online Monitoring of Dibutyl Phosphate Degradation Product in Tributyl Phosphate/n-Dodecane/Nitric Acid Solvent

    SciTech Connect

    Tatiana G. Levitskaia; James M. Peterson; Emily L. Campbell; Amanda J. Casella; Dean R. Peterman; Samuel A. Bryan

    2013-12-01

    In liquid–liquid extraction separation processes, accumulation of organic solvent degradation products is detrimental to the process robustness, and frequent solvent analysis is warranted. Our research explores the feasibility of online monitoring of the organic solvents relevant to used nuclear fuel reprocessing. This paper describes the first phase of developing a system for monitoring the tributyl phosphate (TBP)/n-dodecane solvent commonly used to separate used nuclear fuel. In this investigation, the effect of extraction of nitric acid from aqueous solutions of variable concentrations on the quantification of TBP and its major degradation product dibutylphosphoric acid (HDBP) was assessed. Fourier transform infrared (FTIR) spectroscopy was used to discriminate between HDBP and TBP in the nitric acid-containing TBP/n-dodecane solvent. Multivariate analysis of the spectral data facilitated the development of regression models for HDBP and TBP quantification in real time, enabling online implementation of the monitoring system. The predictive regression models were validated using TBP/n-dodecane solvent samples subjected to high-dose external ?-irradiation. The predictive models were translated to flow conditions using a hollow fiber FTIR probe installed in a centrifugal contactor extraction apparatus, demonstrating the applicability of the FTIR technique coupled with multivariate analysis for the online monitoring of the organic solvent degradation products.

  4. Electrical condition monitoring method for polymers

    DOEpatents

    Watkins, Jr., Kenneth S.; Morris, Shelby J.; Masakowski, Daniel D.; Wong, Ching Ping; Luo, Shijian

    2008-08-19

    An electrical condition monitoring method utilizes measurement of electrical resistivity of an age sensor made of a conductive matrix or composite disposed in a polymeric structure such as an electrical cable. The conductive matrix comprises a base polymer and conductive filler. The method includes communicating the resistivity to a measuring instrument and correlating resistivity of the conductive matrix of the polymeric structure with resistivity of an accelerated-aged conductive composite.

  5. Quaternion Based Thermal Condition Monitoring System

    NASA Astrophysics Data System (ADS)

    Wong, Wai Kit; Loo, Chu Kiong; Lim, Way Soong; Tan, Poi Ngee

    In this paper, we will propose a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. In simulation, we also discover that log-polar mapping actually help solving rotation and scaling invariant problems in quaternion based thermal image correlation. Beside that, log-polar mapping can have a two fold of data compression capability. Log-polar mapping can help smoother up the output correlation plane too, hence makes a better measurement way for PSR and p-values. Simulation results also show that the proposed system is an efficient machine condition monitoring system with accuracy more than 98%.

  6. Monitoring of pigmented and wooden surfaces in accelerated ageing processes by FT-Raman spectroscopy and multivariate control charts.

    PubMed

    Marengo, Emilio; Robotti, Elisa; Liparota, Maria Cristina; Gennaro, Maria Carla

    2004-07-01

    Two of the most suitable analytical techniques used in the field of cultural heritage are NIR (near-infrared) and Raman spectroscopy. FT-Raman spectroscopy coupled to multivariate control charts is applied here for the development of a new method for monitoring the conservation state of pigmented and wooden surfaces. These materials were exposed to different accelerated ageing processes in order to evaluate the effect of the applied treatments on the goods surfaces. In this work, a new approach based on the principles of statistical process control (SPC) to the monitoring of cultural heritage, has been developed: the conservation state of samples simulating works-of-art has been treated like an industrial process, monitored with multivariate control charts, owing to the complexity of the spectroscopic data collected. The Raman spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart and cumulative sum (CUSUM) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surfaces. CUSUM charts however proved to be more effective in the identification of the exact beginning of the applied treatment. In the case of wooden boards, where a sufficient number of PCs were available, simultaneous scores monitoring and residuals tracking (SMART) charts were also investigated. The exposure to a basic attack and to high temperatures produced deep changes on the wooden samples, clearly identified by the multivariate Shewhart, CUSUM and SMART charts. A change on the pigment surface was detected after exposure to an acidic solution and to the UV light, while no effect was identified on the painted surface after the exposure to natural atmospheric events. PMID:18969526

  7. Condition monitoring system of wind turbine generators

    NASA Astrophysics Data System (ADS)

    Abdusamad, Khaled B.

    The development and implementation of the condition monitoring systems (CMS) play a significant role in overcoming the number of failures in the wind turbine generators that result from the harsh operation conditions, such as over temperature, particularly when turbines are deployed offshore. In order to increase the reliability of the wind energy industry, monitoring the operation conditions of wind generators is essential to detect the immediate faults rapidly and perform appropriate preventative maintenance. CMS helps to avoid failures, decrease the potential shutdowns while running, reduce the maintenance and operation costs and maintain wind turbines protected. The knowledge of wind turbine generators' faults, such as stator and rotor inter-turn faults, is indispensable to perform the condition monitoring accurately, and assist with maintenance decision making. Many techniques are utilized to avoid the occurrence of failures in wind turbine generators. The majority of the previous techniques that are applied to monitor the wind generator conditions are based on electrical and mechanical concepts and theories. An advanced CMS can be implemented by using a variety of different techniques and methods to confirm the validity of the obtained electrical and mechanical condition monitoring algorithms. This thesis is focused on applying CMS on wind generators due to high temperature by contributing the statistical, thermal, mathematical, and reliability analyses, and mechanical concepts with the electrical methodology, instead of analyzing the electrical signal and frequencies trends only. The newly developed algorithms can be compared with previous condition monitoring methods, which use the electrical approach in order to establish their advantages and limitations. For example, the hazard reliability techniques of wind generators based on CMS are applied to develop a proper maintenance strategy, which aims to extend the system life-time and reduce the potential

  8. Condition Monitoring of Large-Scale Facilities

    NASA Technical Reports Server (NTRS)

    Hall, David L.

    1999-01-01

    This document provides a summary of the research conducted for the NASA Ames Research Center under grant NAG2-1182 (Condition-Based Monitoring of Large-Scale Facilities). The information includes copies of view graphs presented at NASA Ames in the final Workshop (held during December of 1998), as well as a copy of a technical report provided to the COTR (Dr. Anne Patterson-Hine) subsequent to the workshop. The material describes the experimental design, collection of data, and analysis results associated with monitoring the health of large-scale facilities. In addition to this material, a copy of the Pennsylvania State University Applied Research Laboratory data fusion visual programming tool kit was also provided to NASA Ames researchers.

  9. 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.

  10. Condition Monitoring of Cables Task 3 Report: Condition Monitoring Techniques for Electric Cables

    SciTech Connect

    Villaran, M.; Lofaro, R.; na

    2009-11-30

    For more than 20 years the NRC has sponsored research studying electric cable aging degradation, condition monitoring, and environmental qualification testing practices for electric cables used in nuclear power plants. This report summarizes several of the most effective and commonly used condition monitoring techniques available to detect damage and measure the extent of degradation in electric cable insulation. The technical basis for each technique is summarized, along with its application, trendability of test data, ease of performing the technique, advantages and limitations, and the usefulness of the test results to characterize and assess the condition of electric cables.

  11. Intelligent monitoring of ball bearing conditions

    NASA Astrophysics Data System (ADS)

    Liu, T. I.; Mengel, J. M.

    1992-09-01

    Ball bearings are widely used in various kinds of robots, manufacturing machines, and equipment. In order to enhance productivity and improve product quality, an on-line monitoring system is essential to check the status of ball bearings. In this work, peak amplitude in the frequency domain, peak RMS, and the power spectrum were used as indirect indices to develop a system for monitoring and classifying ball bearing defects. These indices were then processed by artificial neural networks. Six different cases of ball bearing states were observed. The data from these observations were then input into neural networks with different architectures to train these neural networks in a learning process. All the trained neural networks are capable of distinguishing the normal bearings from defective bearings with a 100 percent success rate. They can also classify the bearing conditions into six different states with a success rate of up to 97 per cent. The effects of training set sizes and neural network structures on the monitoring performance have also been investigated.

  12. Using Patterns for Multivariate Monitoring and Feedback Control of Linear Accelerator Performance: Proof-of-Concept Research

    SciTech Connect

    Cordes, Gail Adele; Van Ausdeln, Leo Anthony; Velasquez, Maria Elena

    2002-04-01

    The report discusses preliminary proof-of-concept research for using the Advanced Data Validation and Verification System (ADVVS), a new INEEL software package, to add validation and verification and multivariate feedback control to the operation of non-destructive analysis (NDA) equipment. The software is based on human cognition, the recognition of patterns and changes in patterns in time-related data. The first project applied ADVVS to monitor operations of a selectable energy linear electron accelerator, and showed how the software recognizes in real time any deviations from the optimal tune of the machine. The second project extended the software method to provide model-based multivariate feedback control for the same linear electron accelerator. The projects successfully demonstrated proof-of-concept for the applications and focused attention on the common application of intelligent information processing techniques.

  13. Intelligent tool condition monitoring in milling operation

    SciTech Connect

    Fu, P.; Hope, A.D.; King, G.A.

    1998-09-01

    One of the most important features of the modern machining system in an `unmanned` factory is to change tools that have been subjected to wear and damage. An integrated system composed of multi-sensors, signal processing device and intelligent decision making plans is a necessary requirement for automatic manufacturing process. An intelligent tool wear monitoring system for milling operation will be introduced in this report. The system is equipped with four kinds of sensors, signal transforming and collecting apparatus and microcomputer. A unique ANN (artificial neural network) driven fuzzy pattern recognition algorithm has been developed from this research. It can fuse the information from multiple sensors and has strong learning and noise suppression ability. This lead to successful tool wear classification under a range of machining conditions.

  14. Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

    PubMed

    Del Giudice, G; Padulano, R; Siciliano, D

    2016-01-01

    The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements. PMID:26901717

  15. Monitoring Polaris and Seeing Conditions at PARI

    NASA Astrophysics Data System (ADS)

    Crawford, April

    2016-01-01

    Pisgah Astronomical Research Institute (PARI) was originally built by NASA to track and collect data from satellites. The location in the Pisgah National Forest was chosen due to the excellent ability of the surrounding mountains to block radio interference and light pollution. The PARI observatory has been monitoring Polaris for over 10 years and has amassed a large collection of images of the star and those surrounding it. While several telescopes have been used throughout the project, we are currently using a Omni XLT Series Celestron and an SBIG ST-8300M CCD camera with a 0.70 arcsecond/pixel ratio. The software is run on Windows, however, we will be making a switch to Linux and implementing a new program to control the camera. The new images, once converted to a usable format (ST10 to FITS), can be automatically fed into an in-house Java program to track the variability of the star and simultaneously determine the seeing conditions experienced on the campus. Since we have several years worth of data, the program will also be used to provide a history of variability and seeing conditions. We ultimately hope to be able to track the possible changes in variability of Polaris, as it's current location on the HR diagram is being studied. The data could also prove valuable for our on-site scientists and many visiting students to study on campus. We are also developing a relative scale for our seeing conditions, accompanied by FWHM measurements in arcseconds that will can be compared to those of surrounding observatories in mountainous areas.

  16. Condition Monitoring for Helicopter Data. Appendix A

    NASA Technical Reports Server (NTRS)

    Wen, Fang; Willett, Peter; Deb, Somnath

    2000-01-01

    In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

  17. 40 CFR 141.625 - Conditions requiring increased monitoring.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 40 Protection of Environment 23 2014-07-01 2014-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Requirements § 141.625 Conditions requiring increased monitoring. (a) If you are required to monitor at...

  18. 40 CFR 141.625 - Conditions requiring increased monitoring.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 40 Protection of Environment 24 2012-07-01 2012-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Requirements § 141.625 Conditions requiring increased monitoring. (a) If you are required to monitor at...

  19. 40 CFR 141.625 - Conditions requiring increased monitoring.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 40 Protection of Environment 23 2011-07-01 2011-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Requirements § 141.625 Conditions requiring increased monitoring. (a) If you are required to monitor at...

  20. 40 CFR 141.625 - Conditions requiring increased monitoring.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 40 Protection of Environment 24 2013-07-01 2013-07-01 false Conditions requiring increased monitoring. 141.625 Section 141.625 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... Requirements § 141.625 Conditions requiring increased monitoring. (a) If you are required to monitor at...

  1. Multivariate statistical analysis of water chemistry conditions in three wastewater stabilization ponds with algae blooms and pH fluctuations.

    PubMed

    Wallace, Jack; Champagne, Pascale; Hall, Geof

    2016-06-01

    The wastewater stabilization ponds (WSPs) at a wastewater treatment facility in eastern Ontario, Canada, have experienced excessive algae growth and high pH levels in the summer months. A full range of parameters were sampled from the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. The study presents a novel approach for exploratory analysis of a comprehensive water chemistry dataset, incorporating principal components analysis (PCA) and principal components (PC) and partial least squares (PLS) regressions. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and weak and negative correlations between chl-a and pH and chl-a and DO. PCA reduced the data from 19 to 8 variables, with a good fit to the original data matrix (similarity measure of 0.73). Multivariate regressions to model system pH in terms of these key parameters were performed on the reduced variable set and the PCs generated, for which strong fits (R(2) > 0.79 with all data) were observed. The methodologies presented in this study are applicable to a wide range of natural and engineered systems where a large number of water chemistry parameters are monitored resulting in the generation of large data sets. PMID:27038585

  2. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods.

    PubMed

    Wang, Yiyi; Kockelman, Kara M

    2013-11-01

    This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. PMID:24036167

  3. Validation of the Enterococci indicator for bacteriological quality monitoring of beaches in Malaysia using a multivariate approach.

    PubMed

    Ahmad, Asmat; Dada, Ayokunle C; Usup, Gires; Heng, Lee Y

    2013-01-01

    There is currently no established bacteriological beach quality monitoring (BQM) program in place in Malaysia. To initiate cost-effective, sustainable bacteriological BQM schemes for the ultimate goal of protecting public health, policy decision makers need to be provided robust, indigenous empirical findings that validate appropriate water quality parameters for inclusion in such monitoring programs. This is the first study that assesses the validity of enterococci as an ideal indicator for bacteriological BQM in Malaysia using a multivariate approach. Beach water and sand samples from 7 beach locations were analyzed for a total of twenty-one microbial and non-microbial water quality parameters. A multivariate approach incorporating cluster analyses (CA), principal component analyses (PCA), and factor analysis (FA) was also adopted. Apart from the weak correlations of Staphylococcus aureus with concentrations of Vibro species (r = 0.302, p = 0.037) and total coliforms (r = 0.392, p = 0.006) in seawater, no correlation existed between S. aureus concentration and other parameters. Faecal coliforms failed to correlate with any of the tested parameters. Enterococci also correlated with more quality parameters than faecal coliforms or any other indicator. Multiple linear regressions highlighted a significant, best fit model that could predict enterococci concentrations in relation to other parameters with a maximum predictive success of 69.64%. PCA/FA clearly delineated enterococci and faecal coliforms as parameters that weighed strongly for BQM while Staphylococcus aureus, faecal coliforms and enterococci weighed strongly for beach sand quality monitoring. On the whole, higher correlations of enterococci levels with other parameters than was observed for faecal coliforms suggest that the former be considered a preferred parameter of choice for BQM in Malaysia. Our findings provide meaningful evidence particularly as it relates to the correlation of

  4. Water O–H Stretching Raman Signature for Strong Acid Monitoring via Multivariate Analysis

    SciTech Connect

    Casella, Amanda J.; Levitskaia, Tatiana G.; Peterson, James M.; Bryan, Samuel A.

    2013-04-16

    Spectroscopic techniques have been applied extensively for quantification and analysis of solution compositions. In addition to static measurements, these techniques have been implemented in flow systems providing real-time solution information. A distinct need exists for information regarding acid concentration as it affects extraction efficiency and selectivity of many separation processes. Despite of the seeming simplicity of the problem, no practical solution has been offered yet particularly for the large-scale schemes involving toxic streams such as highly radioactive nuclear wastes. Classic potentiometric technique is not amiable for on-line measurements in nuclear fuel reprocessing due to requirements of frequent calibration/maintenance and poor long-term stability in the aggressive chemical and radiation environments. In this work, the potential of using Raman spectroscopic measurements for on-line monitoring of strong acid concentration in the solutions relevant to the dissolved used fuel was investigated. The Raman water signature was monitored and recorded for nitric and hydrochloric acid solution systems of systematically varied chemical composition, ionic strength, and temperature. The generated Raman spectroscopic database was used to develop predictive chemometric models for the quantification of the acid concentration (H+), neodymium concentration (Nd3+), nitrate concentration (NO3-), density, and ionic strength. This approach was validated using a flow solvent extraction system.

  5. Multivariate analysis comparing microbial air content of an air-conditioned building and a naturally ventilated building over one year

    NASA Astrophysics Data System (ADS)

    Parat, Sylvie; Perdrix, Alain; Fricker-Hidalgo, Hélène; Saude, Isabelle; Grillot, Renee; Baconnier, Pierre

    Heating, ventilation and air-conditioning (HVAC) may be responsible for the production and spread of airborne microorganisms in office buildings. In order to compare airborne microbiological flora in an air-conditioned building with that in a naturally ventilated building, eight sets of measurements were made over a 1-year period. Concurrently with other environmental measurements, air samples were collected in each building, from three offices and from the outdoor air, using the Andersen single-stage sampler. Three different media were used to culture fungi, staphylococci and mesophilic bacteria. Multivariate analysis revealed a group of offices more contaminated than others, and a marked seasonal variation in fungal concentrations. A comparison of mean levels of microorganisms measured in the two buildings showed that the air microbial content was significantly higher and more variable in the naturally ventilated building than in the air-conditioned building. Moreover, in the naturally ventilated building, the interior fungal content was strongly dependent on the outdoor content, while in the air-conditioned building fungal concentrations remained constant despite significant variations measured outside. This was confirmed by a statistical comparison of the correlation coefficients between indoor and outdoor concentrations. No difference was observed regarding gaseous pollutants and temperature, but relative humidity was significantly higher in the air-conditioned building. The effect of HVAC was to prevent the intake of outdoor particles and to dilute the indoor concentrations. These results are consistent with the presence of high-efficiency filters and a steam humidifier in the HVAC system under study.

  6. Technique based on LED multispectral imaging and multivariate analysis for monitoring the conservation state of the Dead Sea Scrolls.

    PubMed

    Marengo, Emilio; Manfredi, Marcello; Zerbinati, Orfeo; Robotti, Elisa; Mazzucco, Eleonora; Gosetti, Fabio; Bearman, Greg; France, Fenella; Shor, Pnina

    2011-09-01

    The aim of this project is the development of a noninvasive technique based on LED multispectral imaging (MSI) for monitoring the conservation state of the Dead Sea Scrolls (DSS) collection. It is well-known that changes in the parchment reflectance drive the transition of the scrolls from legible to illegible. Capitalizing on this fact, we will use spectral imaging to detect changes in the reflectance before they become visible to the human eye. The technique uses multivariate analysis and statistical process control theory. The present study was carried out on a "sample" parchment of calfskin. The monitoring of the surface of a commercial modern parchment aged consecutively for 2 h and 6 h at 80 °C and 50% relative humidity (ASTM) was performed at the Imaging Lab of the Library of Congress (Washington, DC, U.S.A.). MSI is here carried out in the vis-NIR range limited to 1 μm, with a number of bands of 13 and bandwidths that range from about 10 nm in UV to 40 nm in IR. Results showed that we could detect and locate changing pixels, on the basis of reflectance changes, after only a few "hours" of aging. PMID:21777009

  7. Predictive modeling in Clostridium acetobutylicum fermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

    NASA Astrophysics Data System (ADS)

    Zu, Theresah N. K.; Liu, Sanchao; Germane, Katherine L.; Servinsky, Matthew D.; Gerlach, Elliot S.; Mackie, David M.; Sund, Christian J.

    2016-05-01

    The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the well-known Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q2Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.

  8. Support vector machine in machine condition monitoring and fault diagnosis

    NASA Astrophysics Data System (ADS)

    Widodo, Achmad; Yang, Bo-Suk

    2007-08-01

    Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

  9. Wind Turbine Drivetrain Condition Monitoring - An Overview (Presentation)

    SciTech Connect

    Sheng, S.; Yang, W.

    2013-07-01

    High operation and maintenance costs still hamper the development of the wind industry despite its quick growth worldwide. To reduce unscheduled downtime and avoid catastrophic failures of wind turbines and their components have been and will be crucial to further raise the competitiveness of wind power. Condition monitoring is one of the key tools for achieving such a goal. To enhance the research and development of advanced condition monitoring techniques dedicated to wind turbines, we present an overview of wind turbine condition monitoring, discuss current practices, point out existing challenges, and suggest possible solutions.

  10. Monitoring breath markers under controlled conditions.

    PubMed

    Righettoni, Marco; Ragnoni, Alessandro; Güntner, Andreas T; Loccioni, Claudio; Pratsinis, Sotiris E; Risby, Terence H

    2015-12-01

    Breath analysis has the potential to detect and monitor diseases as well as to reduce the corresponding medical costs while improving the quality of a patient's life. Herein, a portable prototype, consisting of a commercial breath sampler modified to work as a platform for solid-state gas sensors was developed. The sensor is placed close to the mouth (<10 cm) and minimizes the mouth-to-sensor path to avoid contamination and dilution of the target breath marker. Additionally with an appropriate cooling concept, even high sensor operating temperatures (e.g. 350 °C) could be used. Controlled sampling is crucial for accurate repeatable analysis of the human breath and these concerns have been addressed by this novel prototype. The device helps a subject control their exhaled flow rate which increases reproducibility of intra-subject breath samples. The operation of this flame-made selective chemo-resistive gas sensor is demonstrated by the detection of breath acetone. PMID:26469378

  11. Using the motor to monitor pump conditions

    SciTech Connect

    Casada, D.

    1996-12-01

    When the load of a mechanical device being driven by a motor changes, whether in response to changes in the overall process or changes in the performance of the driven device, the motor inherently responds. For induction motors, the current amplitude and phase angle change as the shaft load changes. By examining the details of these changes in amplitude and phase, load fluctuations of the driven device can be observed. The usefulness of the motor as a transducer to improve the understanding of devices with high torque fluctuations, such as positive displacement compressors and motor-operated valves, has been recognized and demonstrated for a number of years. On such devices as these, the spectrum of the motor current amplitude, phase, or power normally has certain characteristic peaks associated with various load components, such as the piston stroke or gear tooth meshing frequencies. Comparison and trending of the amplitudes of these peaks has been shown to provide some indication of their mechanical condition. For most centrifugal pumps, the load fluctuations are normally low in torque amplitude, and as a result, the motor experiences a correspondingly lower level of load fluctuation. However, both laboratory and field test data have demonstrated that the motor does provide insight into some important pump performance conditions, such as hydraulic stability and pump-to-motor alignment. Comparisons of other dynamic signals, such as vibration and pressure pulsation, to motor data for centrifugal pumps are provided. The effects of inadequate suction head, misalignment, mechanical and hydraulic unbalance on these signals are presented.

  12. REGIONAL MONITORING OF CORAL CONDITION IN THE FLORIDA KEYS

    EPA Science Inventory

    Fisher, William S. and Deborah L. Santavy. 2004. Regional Monitoring of Coral Condition in Florida Keys (Abstract). Presented at the Monitoring Science and Technology Symposium, 20-24 September 2004, Denver, CO. 1 p. (ERL,GB R1020).

    Coral reefs have experienced unpreceden...

  13. Investigation of Various Wind Turbine Drivetrain Condition Monitoring Techniques (Presentation)

    SciTech Connect

    Sheng, S.

    2011-08-01

    This presentation was given at the 2011 Wind Turbine Reliability Workshop sponsored by Sandia National Laboratories in Albuquerque, NM on August 2-3, 2011. It discusses work for the Gearbox Reliability Collaborative including downtime caused by turbine subsystems, annual failure frequency of turbine subsystems, cost benefits of condition monitoring (CM), the Gearbox Reliability Collaborative's condition monitoring approach and rationale, test setup, and results and observations.

  14. Realtime Monitoring of the Extreme Oceanic Conditions in the Kangjin Bay, South Sea, Korea

    NASA Astrophysics Data System (ADS)

    Ro, Y.; Jung, K.

    2006-05-01

    Realtime(RT) monitoring system for the oceanic state variables was developed and has been operating since April, 2004 in the Kangjin Bay, South Sea, Korea shown. The RT production of data stream and display on the Internet web page are made possible in continuous functions of various system elements. Detailed technical information for the RT monitoring system can be referred to Ro et al. (2004). The water quality parameters, current and meteorological conditions are continuously monitored with very high sampling resolution (10 min.) throughout the year and are being published on the Internet web pages (http://oceaninfo.co.kr/kangjin). The research project encompass several important subjects focusing on the extreme oceanic conditions such as very cold water mass formation during the wintertime cold outbreak, highly diluted sea water during the dam water discharge in the summertime monsoon and the subsequent formation of the hypoxia in the shallow Kangjin Bay. These are the typical extreme events captured possibly by the RT monitoring system, without which could never have been observed and understood. These extreme conditions would exert dramatic ecological impact to the local aqua-culture ecology. This study would elucidate the series of physico-chemical processes and its implication of the local eco-system. To understand the complicated processes, various research tools have been employed such as numerical modeling of tidal circulation, density-driven current, water-quality and formation of hypoxia, time series analyses of various water quality properties including multi-variate correlation.

  15. The CMS Beam Conditions and Radiation Monitoring System

    NASA Astrophysics Data System (ADS)

    Castro, E.; Bacchetta, N.; Bell, A. J.; Dabrowski, A.; Guthoff, M.; Hall-Wilton, R.; Hempel, M.; Henschel, H.; Lange, W.; Lohmann, W.; Müller, S.; Novgorodova, O.; Pfeiffer, D.; Ryjov, V.; Stickland, D.; Schimdt, R.; Walsh, R.

    The Compact Muon Solenoid (CMS) is one of the two large, general purpose experiments situated at the LHC at CERN. As with all high energy physics experiments, knowledge of the beam conditions and luminosity is of vital importance. The Beam Conditions and Radiation Monitoring System (BRM) is installed in CMS to protect the detector and to provide feedback to LHC on beam conditions. It is composed of several sub-systems that measure the radiation level close to or inside all sub-detectors, monitor the beam halo conditions with different time resolution, support beam tuning and protect CMS in case of adverse beam conditions by firing a beam abort signal. This paper presents three of the BRM subsystems: the Fast Beam Conditions Monitor (BCM1F), which is designed for fast flux monitoring, measuring with nanosecond time resolution, both the beam halo and collision products; the Beam Scintillator Counters (BSC), that provide hit rates and time information of beam halo and collision products; and the Beam Conditions Monitors (BCM) used as a protection system that can trigger a beam dump when beam losses occur in order to prevent damage to the pixel and tracker detectors. A description of the systems and a characterization on the basis of data collected during LHC operation is presented.

  16. GEOGLAM Crop Monitor Assessment Tool: Developing Monthly Crop Condition Assessments

    NASA Astrophysics Data System (ADS)

    McGaughey, K.; Becker Reshef, I.; Barker, B.; Humber, M. L.; Nordling, J.; Justice, C. O.; Deshayes, M.

    2014-12-01

    The Group on Earth Observations (GEO) developed the Global Agricultural Monitoring initiative (GEOGLAM) to improve existing agricultural information through a network of international partnerships, data sharing, and operational research. This presentation will discuss the Crop Monitor component of GEOGLAM, which provides the Agricultural Market Information System (AMIS) with an international, multi-source, and transparent consensus assessment of crop growing conditions, status, and agro-climatic conditions likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soybean) within the main agricultural producing regions of the AMIS countries. These assessments have been produced operationally since September 2013 and are published in the AMIS Market Monitor Bulletin. The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type. This presentation will focus on the building of international networks, data collection, and data dissemination.

  17. Spatial and temporal information fusion for crop condition monitoring

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop growth condition information is critical for crop management and yield estimation. In order to monitor crop conditions from space, high spatial and temporal resolution remote sensing data are required. Data fusion approach provides a way to generate such data set from multiple remote sensing da...

  18. Data-driven nonlinear technique for condition monitoring

    SciTech Connect

    Hively, L.M.

    1997-04-01

    This paper describes a sensitive technique for distinguishing changes in a nonlinear process. The method obtains a phase-space (PS) representation of the process, which in turn is converted into a probability density function (PDF). Condition change is monitored by comparing two PS-PDFs via a {chi}{sup 2} statistical measure. One example application involves monitoring of brain waves to distinguish various states in an epileptic patient. A second example distinguishes different drilling conditions from spindle motor current data. A third example distinguishes balanced and unbalanced pumping conditions from power data.

  19. System and method for statistically monitoring and analyzing sensed conditions

    DOEpatents

    Pebay, Philippe P.; Brandt, James M. , Gentile; Ann C. , Marzouk; Youssef M. , Hale; Darrian J. , Thompson; David C.

    2010-07-13

    A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

  20. System and method for statistically monitoring and analyzing sensed conditions

    DOEpatents

    Pebay, Philippe P.; Brandt, James M.; Gentile, Ann C.; Marzouk, Youssef M.; Hale, Darrian J.; Thompson, David C.

    2011-01-25

    A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

  1. System and method for statistically monitoring and analyzing sensed conditions

    DOEpatents

    Pebay, Philippe P.; Brandt, James M.; Gentile, Ann C.; Marzouk, Youssef M.; Hale, Darrian J.; Thompson, David C.

    2011-01-04

    A system and method of monitoring and analyzing a plurality of attributes for an alarm condition is disclosed. The attributes are processed and/or unprocessed values of sensed conditions of a collection of a statistically significant number of statistically similar components subjected to varying environmental conditions. The attribute values are used to compute the normal behaviors of some of the attributes and also used to infer parameters of a set of models. Relative probabilities of some attribute values are then computed and used along with the set of models to determine whether an alarm condition is met. The alarm conditions are used to prevent or reduce the impact of impending failure.

  2. Machine Condition Monitoring Software Agent Using JADE and Data Mining

    NASA Astrophysics Data System (ADS)

    Anandan, R.

    2015-03-01

    In recent days there is a huge demand to increase the production of any mechanical components without any disturbance or mechanical faults in the machine. Therefore, to increase the productivity, it is necessary to monitor the running machine at regular intervals. To overcome such difficulties, a new machine condition monitoring software is designed using the multi agent software. This software is designed using the JADE framework and the data are analyzed using free open source Weka explorer for statistical calculations.

  3. Condition Monitoring of Helicopter Gearboxes by Embedded Sensing

    NASA Technical Reports Server (NTRS)

    Suryavanashi, Abhijit; Wang, Shengda; Gao, Robert; Danai, Kourosh; Lewicki, David G.

    2002-01-01

    Health of helicopter gearboxes is commonly assessed by monitoring the housing vibration, thus it is challenged by poor signal-to-noise ratio of the signal measured away from the source. It is hypothesized that vibration measurements from sensors placed inside the gearbox will be much clearer indicators of faults and will eliminate many of the difficulties faced by present condition monitoring systems. This paper outlines our approach to devising such a monitoring system. Several tasks have been outlined toward this objective and the strategy to address each has been described. Among the tasks are wireless sensor design, antenna design, and selection of sensor locations.

  4. Wireless pilot monitoring system for extreme race conditions.

    PubMed

    Pino, Esteban J; Arias, Diego E; Aqueveque, Pablo; Melin, Pedro; Curtis, Dorothy W

    2012-01-01

    This paper presents the design and implementation of an assistive device to monitor car drivers under extreme conditions. In particular, this system is designed in preparation for the 2012 Atacama Solar Challenge to be held in the Chilean desert. Actual preliminary results show the feasibility of such a project including physiological and ambient sensors, real-time processing algorithms, wireless data transmission and a remote monitoring station. Implementation details and field results are shown along with a discussion of the main problems found in real-life telemetry monitoring. PMID:23367054

  5. Model based condition monitoring in lithium-ion batteries

    NASA Astrophysics Data System (ADS)

    Singh, Amardeep; Izadian, Afshin; Anwar, Sohel

    2014-12-01

    In this paper, a model based condition monitoring technique is developed for lithium-ion battery condition monitoring. Here a number of lithium-ion batteries are cycled using two separate over discharge test regimes and the resulting shift in battery parameters is recorded. The battery models are constructed using the equivalent circuit methodology. The condition monitoring setup consists of a model bank representing the different degree of parameter shift due to overdischarge in the lithium ion battery. Extended Kalman filters (EKF) are used to maintain increased robustness of the condition monitoring setup while estimating the terminal voltage of the battery cell. The information carrying residuals are generated and evaluation process is carried out in real-time using multiple model adaptive estimation (MMAE) methodology. The condition evaluation function is used to generate probabilities that indicate the presence of a particular operational condition. Using the test data, it is shown that the performance shift in lithium ion batteries due to over discharge can be accurately detected.

  6. Tolkku - a toolbox for decision support from condition monitoring data

    NASA Astrophysics Data System (ADS)

    Saarela, Olli; Lehtonen, Mikko; Halme, Jari; Aikala, Antti; Raivio, Kimmo

    2012-05-01

    This paper describes a software toolbox (a software library) designed for condition monitoring and diagnosis of machines. This toolbox implements both new methods and prior art and is aimed for practical down-to-earth data analysis work. The target is to improve knowledge of the operation and behaviour of machines and processes throughout their entire life-cycles. The toolbox supports different phases of condition based maintenance with tools that extract essential information and automate data processing. The paper discusses principles that have guided toolbox design and the implemented toolbox structure. Case examples are used to illustrate how condition monitoring applications can be built using the toolbox. In the first case study the toolbox is applied to fault detection of industrial centrifuges based on measured electrical current. The second case study outlines an application for centralized monitoring of a fleet of machines that supports organizational learning.

  7. Wind Turbine Gearbox Oil Filtration and Condition Monitoring

    SciTech Connect

    Sheng, Shuangwen

    2015-10-25

    This is an invited presentation for a pre-conference workshop, titled advances and opportunities in lubrication: wind turbine, at the 2015 Society of Tribologists and Lubrication Engineers (STLE) Tribology Frontiers Conference held in Denver, CO. It gives a brief overview of wind turbine gearbox oil filtration and condition monitoring by highlighting typical industry practices and challenges. The presentation starts with an introduction by covering recent growth of global wind industry, reliability challenges, benefits of oil filtration and condition monitoring, and financial incentives to conduct wind operation and maintenance research, which includes gearbox oil filtration and condition monitoring work presented herein. Then, the presentation moves on to oil filtration by stressing the benefits of filtration, discussing typical main- and offline-loop practices, highlighting important factors considered when specifying a filtration system, and illustrating real-world application challenges through a cold-start example. In the next section on oil condition monitoring, a discussion on oil sample analysis, oil debris monitoring, oil cleanliness measurements and filter analysis is given based on testing results mostly obtained by and at NREL, and by pointing out a few challenges with oil sample analysis. The presentation concludes with a brief touch on future research and development (R and D) opportunities. It is hoping that the information presented can inform the STLE community to start or redirect their R and D work to help the wind industry advance.

  8. An overview of crop growing condition monitoring in China agriculture remote sensing monitoring system

    NASA Astrophysics Data System (ADS)

    Huang, Qing; Zhou, Qing-bo; Zhang, Li

    2009-07-01

    China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.

  9. A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring

    SciTech Connect

    Liao, T. W.; Ting, C.F.; Qu, Jun; Blau, Peter Julian

    2007-01-01

    Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish different states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the decomposition level, and the GA parameters are properly selected.

  10. Aero-Engine Condition Monitoring Based on Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhang, Chunxiao; Wang, Nan

    The maintenance and management of civil aero-engine require advanced monitor approaches to estimate aero-engine performance and health in order to increase life of aero-engine and reduce maintenance costs. In this paper, we adopted support vector machine (SVM) regression approach to monitor an aero-engine health and condition by building monitoring models of main aero-engine performance parameters(EGT, N1, N2 and FF). The accuracy of nonlinear baseline models of performance parameters is tested and the maximum relative error does not exceed ±0.3%, which meets the engineering requirements. The results show that SVM nonlinear regression is an effective method in aero-engine monitoring.

  11. Reality Monitoring and Metamemory in Adults with Autism Spectrum Conditions.

    PubMed

    Cooper, Rose A; Plaisted-Grant, Kate C; Baron-Cohen, Simon; Simons, Jon S

    2016-06-01

    Studies of reality monitoring (RM) often implicate medial prefrontal cortex (mPFC) in distinguishing internal and external information, a region linked to autism-related deficits in social and self-referential information processing, executive function, and memory. This study used two RM conditions (self-other; perceived-imagined) to investigate RM and metamemory in adults with autism. The autism group showed a deficit in RM, which did not differ across source conditions, and both groups exhibited a self-encoding benefit on recognition and source memory. Metamemory for perceived-imagined information, but not for self-other information, was significantly lower in the autism group. Therefore, reality monitoring and metamemory, sensitive to mPFC function, appear impaired in autism, highlighting a difficulty in remembering and monitoring internal and external details of past events. PMID:26899724

  12. Energy harvesting to power embedded condition monitoring hardware

    NASA Astrophysics Data System (ADS)

    Farinholt, Kevin; Brown, Nathan; Siegel, Jake; McQuown, Justin; Humphris, Robert

    2015-04-01

    The shift toward condition-based monitoring is a key area of research for many military, industrial, and commercial customers who want to lower the overall operating costs of capital equipment and general facilities. Assessing the health of rotating systems such as gearboxes, bearings, pumps and other actuation systems often rely on the need for continuous monitoring to capture transient signals that are evidence of events that could cause (i.e. cavitation), or be the result of (i.e. spalling), damage within a system. In some applications this can be accomplished using line powered analyzers, however for wide-spread monitoring, the use of small-scale embedded electronic systems are more desirable. In such cases the method for powering the electronics becomes a significant design factor. This work presents a multi-source energy harvesting approach meant to provide a robust power source for embedded electronics, capturing energy from vibration, thermal and light sources to operate a low-power sensor node. This paper presents the general design philosophy behind the multi-source harvesting circuit, and how it can be extended from powering electronics developed for periodic monitoring to sensing equipment capable of providing continuous condition-based monitoring.

  13. USING CONDITION MONITORING TO PREDICT REMAINING LIFE OF ELECTRIC CABLES.

    SciTech Connect

    LOFARO,R.; SOO,P.; VILLARAN,M.; GROVE,E.

    2001-03-29

    Electric cables are passive components used extensively throughout nuclear power stations to perform numerous safety and non-safety functions. It is known that the polymers commonly used to insulate the conductors on these cables can degrade with time; the rate of degradation being dependent on the severity of the conditions in which the cables operate. Cables do not receive routine maintenance and, since it can be very costly, they are not replaced on a regular basis. Therefore, to ensure their continued functional performance, it would be beneficial if condition monitoring techniques could be used to estimate the remaining useful life of these components. A great deal of research has been performed on various condition monitoring techniques for use on electric cables. In a research program sponsored by the U.S. Nuclear Regulatory Commission, several promising techniques were evaluated and found to provide trendable information on the condition of low-voltage electric cables. These techniques may be useful for predicting remaining life if well defined limiting values for the aging properties being measured can be determined. However, each technique has advantages and limitations that must be addressed in order to use it effectively, and the necessary limiting values are not always easy to obtain. This paper discusses how condition monitoring measurements can be used to predict the remaining useful life of electric cables. The attributes of an appropriate condition monitoring technique are presented, and the process to be used in estimating the remaining useful life of a cable is discussed along with the difficulties that must be addressed.

  14. Fast beam condition monitor for CMS: Performance and upgrade

    NASA Astrophysics Data System (ADS)

    Leonard, Jessica L.; Bell, Alan; Burtowy, Piotr; Dabrowski, Anne; Hempel, Maria; Henschel, Hans; Lange, Wolfgang; Lohmann, Wolfgang; Odell, Nathaniel; Penno, Marek; Pollack, Brian; Przyborowski, Dominik; Ryjov, Vladimir; Stickland, David; Walsh, Roberval; Warzycha, Weronika; Zagozdzinska, Agnieszka

    2014-11-01

    The CMS beam and radiation monitoring subsystem BCM1F (Fast Beam Condition Monitor) consists of 8 individual diamond sensors situated around the beam pipe within the pixel detector volume, for the purpose of fast bunch-by-bunch monitoring of beam background and collision products. In addition, effort is ongoing to use BCM1F as an online luminosity monitor. BCM1F will be running whenever there is beam in LHC, and its data acquisition is independent from the data acquisition of the CMS detector, hence it delivers luminosity even when CMS is not taking data. A report is given on the performance of BCM1F during LHC run I, including results of the van der Meer scan and on-line luminosity monitoring done in 2012. In order to match the requirements due to higher luminosity and 25 ns bunch spacing, several changes to the system must be implemented during the upcoming shutdown, including upgraded electronics and precise gain monitoring. First results from Run II preparation are shown.

  15. The Thirty Meter Telescope Site Conditions Monitoring System

    NASA Astrophysics Data System (ADS)

    Skidmore, Warren; Travouillon, Tony

    2015-04-01

    We examine the experiences and ideas from operating observatories regarding the measurements of the characteristics of the atmosphere that must be gathered within the locality of the observatory in order to support safe, efficient and scientifically optimized observatory operations as well as commissioning, performance monitoring and support the scientific analysis of telescope observations. We describe the expected requirements for the measurement capabilities of the the TMT Site Conditions Monitoring System (SCMS) and discuss how these plans are being developed with input from staff at operating observatories and active observational astronomers.

  16. Beam monitoring and conditioning working group 4 report

    SciTech Connect

    Wang, X.J.

    1997-01-01

    The highlights of Seventh Advanced Accelerator Concepts (AAC) working group IV (Beam Monitoring, Conditioning and Control at High Frequencies and Ultrafast Timescales) are presented in this report. The talks given at the working group covered wide range of subjects of beam monitoring. They including a new technique of measuring sub- picosecond electron beam bunch length, optical stochastic cooling experiment, timing jitter measurement of photocathode injector, and proposed experiment of measuring micro-bunching of IFEL accelerator. Working group IV also carried out extensive discussion on the longitudinal and transverse emittance characterization of short (sub- picosecond) low emittance (normalized rms emittance < 1 mm-mrad) electron beam, and beam diagnostics requirements for Muon collider.

  17. A suite of optical fibre sensors for structural condition monitoring

    NASA Astrophysics Data System (ADS)

    Sun, T.; Grattan, K. T. V.; Carlton, J.

    2015-05-01

    This paper is to review the research activities at City University London in the development of a range of fibre Bragg grating (FBG)-based sensors, including strain, temperature, relative humidity, vibration and acoustic sensors, with an aim to meet the increasing demands from industry for structural condition monitoring. As a result, arrays of optical fibre sensors have been instrumented into various types of structures, including concrete, limestone, marine propellers, pantograph and electrical motors, allowing for both static and dynamic monitoring and thus enhanced structural reliability and integrity.

  18. Vibration condition monitoring of planetary gearbox under varying external load

    NASA Astrophysics Data System (ADS)

    Bartelmus, W.; Zimroz, R.

    2009-01-01

    The paper shows that for condition monitoring of planetary gearboxes it is important to identify the external varying load condition. In the paper, systematic consideration has been taken of the influence of many factors on the vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for vibration signal interpretation, development of the means of condition monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It is found that a planetary gearbox in bad condition is more susceptible to load than a gearbox in good condition. The estimated load time traces obtained by a demodulation process of the vibration acceleration signal for a planetary gearbox in good and bad conditions are given. It has been found that the most important factor of the proper planetary gearbox condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the condition of the planetary gearboxes considered. Typical local faults such as cracking or breakage of a gear tooth, or local faults in rolling element bearings, have not been found in the cases considered. In real practice, local faults of planetary gearboxes have not occurred, but heavy destruction of planetary gearboxes have

  19. Bridge condition assessment based on long-term strain monitoring

    NASA Astrophysics Data System (ADS)

    Sun, LiMin; Sun, Shouwang

    2011-04-01

    In consideration of the important role that bridges play as transportation infrastructures, their safety, durability and serviceability have always been deeply concerned. Structural Health Monitoring Systems (SHMS) have been installed to many long-span bridges to provide bridge engineers with the information needed in making rational decisions for maintenance. However, SHMS also confronted bridge engineers with the challenge of efficient use of monitoring data. Thus, methodologies which are robust to random disturbance and sensitive to damage become a subject on which many researches in structural condition assessment concentrate. In this study, an innovative probabilistic approach for condition assessment of bridge structures was proposed on the basis of long-term strain monitoring on steel girder of a cable-stayed bridge. First, the methodology of damage detection in the vicinity of monitoring point using strain-based indices was investigated. Then, the composition of strain response of bridge under operational loads was analyzed. Thirdly, the influence of temperature and wind on strains was eliminated and thus strain fluctuation under vehicle loads is obtained. Finally, damage evolution assessment was carried out based on the statistical characteristics of rain-flow cycles derived from the strain fluctuation under vehicle loads. The research conducted indicates that the methodology proposed is qualified for structural condition assessment so far as the following respects are concerned: (a) capability of revealing structural deterioration; (b) immunity to the influence of environmental variation; (c) adaptability to the random characteristic exhibited by long-term monitoring data. Further examination of the applicability of the proposed methodology in aging bridge may provide a more convincing validation.

  20. Model-based condition monitoring for lithium-ion batteries

    NASA Astrophysics Data System (ADS)

    Kim, Taesic; Wang, Yebin; Fang, Huazhen; Sahinoglu, Zafer; Wada, Toshihiro; Hara, Satoshi; Qiao, Wei

    2015-11-01

    Condition monitoring for batteries involves tracking changes in physical parameters and operational states such as state of health (SOH) and state of charge (SOC), and is fundamentally important for building high-performance and safety-critical battery systems. A model-based condition monitoring strategy is developed in this paper for Lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect. It systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least squares algorithm for estimating the maximum capacity, which indicates the SOH. The proposed solution enjoys advantages including high accuracy, low computational cost, and simple implementation, and therefore is suitable for deployment and use in real-time embedded battery management systems (BMSs). Simulations and experiments validate effectiveness of the proposed strategy.

  1. Monitoring of Double Stud Wall Moisture Conditions in the Northeast

    SciTech Connect

    Ueno, K.

    2015-03-01

    Double-stud walls insulated with cellulose or low-density spray foam can have R-values of 40 or higher. However, double stud walls have a higher risk of interior-sourced condensation moisture damage, when compared with high-R approaches using exterior insulating sheathing.; Moisture conditions in double stud walls were monitored in Zone 5A (Massachusetts); three double stud assemblies were compared.

  2. Monitoring vegetation conditions from LANDSAT for use in range management

    NASA Technical Reports Server (NTRS)

    Haas, R. H.; Deering, D. W.; Rouse, J. W., Jr.; Schell, J. A.

    1975-01-01

    A summary of the LANDSAT Great Plains Corridor projects and the principal results are presented. Emphasis is given to the use of satellite acquired phenological data for range management and agri-business activities. A convenient method of reducing LANDSAT MSS data to provide quantitative estimates of green biomass on rangelands in the Great Plains is explained. Suggestions for the use of this approach for evaluating range feed conditions are presented. A LANDSAT Follow-on project has been initiated which will employ the green biomass estimation method in a quasi-operational monitoring of range readiness and range feed conditions on a regional scale.

  3. Distributed multisensor fusion for machine condition monitoring fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Xue; Zhao, Guohua; Xie, Xin

    2001-09-01

    This paper presents a new general framework for multisensor fusion based on a distributed detection. Parallel processing and distributed multisensor fusion, as rapidly emerging and promising technologies, provides powerful tools for solving this difficult problem, The distribution and parallelism of proposing and confirming of hypothesis in condition and diagnostic is prosed. A combination serial and parallel reconfiguration of n sensors for decision fusion is analyzed. It shows the result for a real-time parallel distributed complex machine condition monitor and fault diagnostic system.

  4. Surface Acoustic Wave (SAW) Resonators for Monitoring Conditioning Film Formation.

    PubMed

    Hohmann, Siegfried; Kögel, Svea; Brunner, Yvonne; Schmieg, Barbara; Ewald, Christina; Kirschhöfer, Frank; Brenner-Weiß, Gerald; Länge, Kerstin

    2015-01-01

    We propose surface acoustic wave (SAW) resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case of implant insertion, for instance, initial protein adsorption is required to start wound healing, but it will also trigger immune reactions leading to inflammatory responses. The control of the initial protein adsorption would allow to promote the healing process and to suppress adverse immune reactions. Methods to investigate these adsorption processes are available, but it remains difficult to translate measurement results into actual protein binding events. Biosensor transducers allow user-friendly investigation of protein adsorption on different surfaces. The combination of several transduction principles leads to complementary results, allowing a more comprehensive characterization of the adsorbing layer. We introduce SAW resonators as a novel complementary tool for time-resolved conditioning film monitoring. SAW resonators were coated with polymers. The adsorption of the plasma proteins human serum albumin (HSA) and fibrinogen onto the polymer-coated surfaces were monitored. Frequency results were compared with quartz crystal microbalance (QCM) sensor measurements, which confirmed the suitability of the SAW resonators for this application. PMID:26007735

  5. Surface Acoustic Wave (SAW) Resonators for Monitoring Conditioning Film Formation

    PubMed Central

    Hohmann, Siegfried; Kögel, Svea; Brunner, Yvonne; Schmieg, Barbara; Ewald, Christina; Kirschhöfer, Frank; Brenner-Weiß, Gerald; Länge, Kerstin

    2015-01-01

    We propose surface acoustic wave (SAW) resonators as a complementary tool for conditioning film monitoring. Conditioning films are formed by adsorption of inorganic and organic substances on a substrate the moment this substrate comes into contact with a liquid phase. In the case of implant insertion, for instance, initial protein adsorption is required to start wound healing, but it will also trigger immune reactions leading to inflammatory responses. The control of the initial protein adsorption would allow to promote the healing process and to suppress adverse immune reactions. Methods to investigate these adsorption processes are available, but it remains difficult to translate measurement results into actual protein binding events. Biosensor transducers allow user-friendly investigation of protein adsorption on different surfaces. The combination of several transduction principles leads to complementary results, allowing a more comprehensive characterization of the adsorbing layer. We introduce SAW resonators as a novel complementary tool for time-resolved conditioning film monitoring. SAW resonators were coated with polymers. The adsorption of the plasma proteins human serum albumin (HSA) and fibrinogen onto the polymer-coated surfaces were monitored. Frequency results were compared with quartz crystal microbalance (QCM) sensor measurements, which confirmed the suitability of the SAW resonators for this application. PMID:26007735

  6. Evaluation of Diesel Exhaust Continuous Monitors in Controlled Environmental Conditions

    PubMed Central

    Yu, Chang Ho; Patton, Allison P.; Zhang, Andrew; Fanac, Zhi-Hua (Tina); Weisel, Clifford P.; Lioy, Paul J.

    2015-01-01

    Diesel exhaust (DE) contains a variety of toxic air pollutants, including diesel particulate matter (DPM) and gaseous contaminants (e.g., carbon monoxide (CO)). DPM is dominated by fine (PM2.5) and ultrafine particles (UFP), and can be representatively determined by its thermal-optical refractory as elemental carbon (EC) or light-absorbing characteristics as black carbon (BC). The currently accepted reference method for sampling and analysis of occupational exposure to DPM is the National Institute for Occupational Safety and Health (NIOSH) Method 5040. However, this method cannot provide in-situ short-term measurements of DPM. Thus, real-time monitors are gaining attention to better examine DE exposures in occupational settings. However, real-time monitors are subject to changing environmental conditions. Field measurements have reported interferences in optical sensors and subsequent real-time readings, under conditions of high humidity and abrupt temperature changes. To begin dealing with these issues, we completed a controlled study to evaluate five real-time monitors: Airtec real-time DPM/EC Monitor, TSI SidePak Personal Aerosol Monitor AM510 (PM2.5), TSI Condensation Particle Counter 3007, microAeth AE51 BC Aethalometer, and Langan T15n CO Measurer. Tests were conducted under different temperatures (55, 70, and 80 °F), relative humidity (10, 40, and 80%), and DPM concentrations (50 and 200 µg/m3) in a controlled exposure facility. The 2-hour averaged EC measurements from the Airtec instrument showed relatively good agreement with NIOSH Method 5040 (R2=0.84; slope=1.17±0.06; N=27) and reported ~17% higher EC concentrations than the NIOSH reference method. Temperature, relative humidity, and DPM levels did not significantly affect relative differences in 2-hour averaged EC concentrations obtained by the Airtec instrument versus the NIOSH method (p<0.05). Multiple linear regression analyses, based on 1-min averaged data, suggested combined effects of up to 5

  7. Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions.

    PubMed

    Yotova, Galina I; Tsitouridou, Roxani; Tsakovski, Stefan L; Simeonov, Vasil D

    2016-01-01

    The present article deals with assessment of urban air by using monitoring data for 10 different aerosol fractions (0.015-16 μm) collected at a typical urban site in City of Thessaloniki, Greece. The data set was subject to multivariate statistical analysis (cluster analysis and principal components analysis) and, additionally, to HYSPLIT back trajectory modeling in order to assess in a better way the impact of the weather conditions on the pollution sources identified. A specific element of the study is the effort to clarify the role of outliers in the data set. The reason for the appearance of outliers is strongly related to the atmospheric condition on the particular sampling days leading to enhanced concentration of pollutants (secondary emissions, sea sprays, road and soil dust, combustion processes) especially for ultra fine and coarse particles. It is also shown that three major sources affect the urban air quality of the location studied-sea sprays, mineral dust and anthropogenic influences (agricultural activity, combustion processes, and industrial sources). The level of impact is related to certain extent to the aerosol fraction size. The assessment of the meteorological conditions leads to defining of four downwind patterns affecting the air quality (Pelagic, Western and Central Europe, Eastern and Northeastern Europe and Africa and Southern Europe). Thus, the present study offers a complete urban air assessment taking into account the weather conditions, pollution sources and aerosol fractioning. PMID:26942452

  8. Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

    SciTech Connect

    Sheng, S.

    2012-07-01

    The Gearbox Reliability Collaborative (GRC) at the National Wind Technology Center (NWTC) tested two identical gearboxes. One was tested on the NWTCs 2.5 MW dynamometer and the other was field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not severe, the test gearbox was removed from the field and retested in the NWTCs dynamometer before it was disassembled. During the dynamometer retest, some vibration data along with testing condition information were collected. These data enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project was to evaluate different vibration analysis algorithms used in wind turbine condition monitoring (CM) and find out whether the typical practices are effective. With involvement of both academic researchers and industrial partners, the project sets an example on providing cutting edge research results back to industry.

  9. Optimization of Remediation Conditions using Vadose Zone Monitoring Technology

    NASA Astrophysics Data System (ADS)

    Dahan, O.; Mandelbaum, R.; Ronen, Z.

    2010-12-01

    Success of in-situ bio-remediation of the vadose zone depends mainly on the ability to change and control hydrological, physical and chemical conditions of subsurface. These manipulations enables the development of specific, indigenous, pollutants degrading bacteria or set the environmental conditions for seeded bacteria. As such, the remediation efficiency is dependent on the ability to implement optimal hydraulic and chemical conditions in deep sections of the vadose zone. Enhanced bioremediation of the vadose zone is achieved under field conditions through infiltration of water enriched with chemical additives. Yet, water percolation and solute transport in unsaturated conditions is a complex process and application of water with specific chemical conditions near land surface dose not necessarily result in promoting of desired chemical and hydraulic conditions in deeper sections of the vadose zone. A newly developed vadose-zone monitoring system (VMS) allows continuous monitoring of the hydrological and chemical properties of the percolating water along deep sections of the vadose zone. Implementation of the VMS at sites that undergoes active remediation provides real time information on the chemical and hydrological conditions in the vadose zone as the remediation process progresses. Manipulating subsurface conditions for optimal biodegradation of hydrocarbons is demonstrated through enhanced bio-remediation of the vadose zone at a site that has been contaminated with gasoline products in Tel Aviv. The vadose zone at the site is composed of 6 m clay layer overlying a sandy formation extending to the water table at depth of 20 m bls. The upper 5 m of contaminated soil were removed for ex-situ treatment, and the remaining 15 m vadose zone is treated in-situ through enhanced bioremedaition. Underground drip irrigation system was installed below the surface on the bottom of the excavation. Oxygen and nutrients releasing powder (EHCO, Adventus) was spread below the

  10. Suitability of MEMS Accelerometers for Condition Monitoring: An experimental study

    PubMed Central

    Albarbar, Alhussein; Mekid, Samir; Starr, Andrew; Pietruszkiewicz, Robert

    2008-01-01

    With increasing demands for wireless sensing nodes for assets control and condition monitoring; needs for alternatives to expensive conventional accelerometers in vibration measurements have been arisen. Micro-Electro Mechanical Systems (MEMS) accelerometer is one of the available options. The performances of three of the MEMS accelerometers from different manufacturers are investigated in this paper and compared to a well calibrated commercial accelerometer used as a reference for MEMS sensors performance evaluation. Tests were performed on a real CNC machine in a typical industrial environmental workshop and the achieved results are presented.

  11. Tunable Vibration Energy Harvester for Condition Monitoring of Maritime Gearboxes

    NASA Astrophysics Data System (ADS)

    Hoffmann, D.; Willmann, A.; Folkmer, B.; Manoli, Y.

    2014-11-01

    This paper reports on a new tuning concept, which enables the operation of a vibration generator for energy autonomous condition monitoring of maritime gearboxes. The tuning concept incorporates a circular tuning magnet, which interacts with a coupling magnet attached to the active transducer element. The tuning range can be tailored to the application by careful design of the gap between tuning magnet and coupling magnet. A total rotation angle of only 180° is required for the tuning magnet in order to obtain the full frequency bandwidth. The tuning concept is successfully demonstrated by charging a 0.6 F capacitor on the basis of physical vibration profiles taken from a gearbox.

  12. The ATLAS Beam Condition and Beam Loss Monitors

    NASA Astrophysics Data System (ADS)

    Dolenc, I.

    2010-04-01

    The primary goal of ATLAS Beam Condition Monitor (BCM) and Beam Loss Monitor (BLM) is to protect the ATLAS Inner Detector against damaging LHC beam incidents by initiating beam abort in case of beam failures. Poly-crystalline Chemical Vapour Deposition (pCVD) diamond was chosen as the sensor material for both systems. ATLAS BCM will provide real-time monitoring of instantaneous particle rates close to the interaction point (IP) of ATLAS spectrometer. Using fast front-end and signal processing electronics the time-of-flight and pulse amplitude measurements will be performed to distinguish between normal collisions and background events due to natural or accidental beam losses. Additionally, BCM will also provide coarse relative luminosity information. A second system, the ATLAS BLM, is an independent system which was recently added to complement the BCM. It is a current measuring system and was partially adopted from the BLM system developed by the LHC beam instrumentation group with pCVD diamond pad sensors replacing the ionisation chambers. The design of both systems and results of operation in ATLAS framework during the commissioning with cosmic rays will be reported in this contribution.

  13. Condition monitoring of reciprocating seal based on FBG sensors

    NASA Astrophysics Data System (ADS)

    Zhao, Xiuxu; Zhang, Shuanshuan; Wen, Pengfei; Zhen, Wenhan; Ke, Wei

    2016-07-01

    The failure of hydraulic reciprocating seals will seriously affect the normal operation of hydraulic reciprocating machinery, so the potential fault condition monitoring of reciprocating seals is very important. However, it is extremely difficult because of the limitation of reciprocating motion and the structure constraints of seal groove. In this study, an approach using fiber Bragg grating (FBG) sensors is presented. Experimental results show that the contact strain changes of a reciprocating seal can be detected by FBG sensors in the operation process of the hydraulic cylinders. The failure condition of the reciprocating seal can be identified by wavelet packet energy entropy, and the center frequency of power spectrum analysis. It can provide an effective solution for the fault prevention and health management of reciprocating hydraulic rod seals.

  14. VegScape: U.S. Crop Condition Monitoring Service

    NASA Astrophysics Data System (ADS)

    mueller, R.; Yang, Z.; Di, L.

    2013-12-01

    Since 1995, the US Department of Agriculture (USDA)/National Agricultural Statistics Service (NASS) has provided qualitative biweekly vegetation condition indices to USDA policymakers and the public on a weekly basis during the growing season. Vegetation indices have proven useful for assessing crop condition and identifying the areal extent of floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. With growing emphasis on more extreme weather events and food security issues rising to the forefront of national interest, a new vegetation condition monitoring system was developed. The new vegetation condition portal named VegScape was initiated at the start of the 2013 growing season. VegScape delivers web mapping service based interactive vegetation indices. Users can use an interactive map to explore, query and disseminate current crop conditions. Vegetation indices like Normal Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and mean, median, and ratio comparisons to prior years can be constructed for analytical purposes and on-demand crop statistics. The NASA MODIS satellite with 250 meter (15 acres) resolution and thirteen years of data history provides improved spatial and temporal resolutions and delivers improved detailed timely (i.e., daily) crop specific condition and dynamics. VegScape thus provides supplemental information to support NASS' weekly crop reports. VegScape delivers an agricultural cultivated crop mask and the most recent Cropland Data Layer (CDL) product to exploit the agricultural domain and visualize prior years' planted crops. Additionally, the data can be directly exported to Google Earth for web mashups or delivered via web mapping services for uses in other applications. VegScape supports the ethos of data democracy by providing free and open access to digital geospatial data layers using open geospatial standards, thereby supporting transparent and collaborative government

  15. Condition Monitoring for Shinkansen Bogies Based on Vibration Analysis

    NASA Astrophysics Data System (ADS)

    Oba, Takuya; Yamada, Koichi; Okada, Nobuyuki; Tanifuji, Katsuya

    Rolling stock has generally been inspected and maintained on the basis of preventive maintenance. However, the reliability of sensors and information technology has drastically improved and, with this background, the objective of this research is to develop a condition monitoring system for the bogies of Shinkansen cars. This paper describes two algorithms for detecting faults in some parts of bogies. These algorithms are based on the statistical analysis of vibration acceleration during some periods. One algorithm detects the difference in the vibration peak distribution between normal operation and operation with faulty parts. The other algorithm compares the vibration states between the front and rear bogies in the same car when one bogie has faulty parts. To examine the details of the vibration characteristics of the bogie with some faults, experiments simulating some faults in bogie parts are conducted in the rolling stock field simulator at Komaki Research Center of JR Central. Through this experiments and analysis, we can demonstrate the reliability and validity of the schemes developed in this study for monitoring the conditions of Shinkansen bogies.

  16. Self-powered sensing for mechanical system condition monitoring

    NASA Astrophysics Data System (ADS)

    Gao, Robert X.; Kazmer, David O.; Zhang, Li; Theurer, Charles B.; Cui, Yong

    2004-07-01

    A self-powered wireless sensing module for the condition monitoring of mechanical systems and high energy manufacturing processes is described, with injection molding as a special application. The design and analysis of three constituent components in such a sensing module: an energy converter consisting of a piezoceramic stack, an energy regulator based on a pair of bipolar transistors, and a piezoelectric transmitter that transmits ultrasound signals proportional to the pressure within the injection mold, are presented in this paper. The energy extraction mechanism is investigated based on the interactions between the mechanical strain and the electric field developed within the piezoceramic stack. Analytical models for the energy modulator and signal transmitter are also established. Quantitative results are obtained that describe the energy flow among the three components and guide the parametric design of the three constituent components. Simulations and experimental studies have validated the functionality of each component. The models established can be used to subsequently optimize the design of the entire sensor module in terms of minimizing the energy requirement for the sensor and identifying the minimum level of signal intensity required to ensure successful detection of the signal by the signal receiver on the outside of the injection mold. The proposed self-powered sensing technique enables a new generation of sensors that can be employed for the condition monitoring and health diagnosis of a wide range of mechanical and civil systems that are characterized by high energy contents.

  17. Condition monitoring of machinery using motor current signature analysis

    SciTech Connect

    Kryter, R.C.; Haynes, H.D.

    1989-01-01

    Motor current signature analysis (MCSA) is a powerful monitoring tool for motor-driven equipment that provides a nonintrusive means for detecting the presence of mechanical and electrical abnormalities in the motor and the driven equipment, including altered conditions in the process ''downstream'' of the motor-driven equipment. It was developed at the Oak Ridge National Laboratory as a means for determining the effects of aging and service wear systems, but it is applicable to a broad range of machinery. MCSA is based on the recognition that an electric motor (ac or dc) driving a mechanical load acts as an efficient and permanently available transducer by sensing mechanical load variations, large and small, long-term and rapid, and converting them into variations in the induced current generated in the motor windings. These motor current variations are carried by the electrical cables processes as desired. Motor current signatures, obtained in both time and over time to provide early indication of degradation. Successful applications of MCSA technology (patent applied for) include not only motor-operated valves but also pumps of various designs, blowers, and air conditioning systems. Examples are presented briefly, and speculation regarding the applicability of MCSA to a broader range of equipment monitoring and production line testing is also given. 1 ref., 13 figs.

  18. 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.

  19. SSME Condition Monitoring Using Neural Networks and Plume Spectral Signatures

    NASA Technical Reports Server (NTRS)

    Hopkins, Randall; Benzing, Daniel

    1996-01-01

    For a variety of reasons, condition monitoring of the Space Shuttle Main Engine (SSME) has become an important concern for both ground tests and in-flight operation. The complexities of the SSME suggest that active, real-time condition monitoring should be performed to avoid large-scale or catastrophic failure of the engine. In 1986, the SSME became the subject of a plume emission spectroscopy project at NASA's Marshall Space Flight Center (MSFC). Since then, plume emission spectroscopy has recorded many nominal tests and the qualitative spectral features of the SSME plume are now well established. Significant discoveries made with both wide-band and narrow-band plume emission spectroscopy systems led MSFC to develop the Optical Plume Anomaly Detection (OPAD) system. The OPAD system is designed to provide condition monitoring of the SSME during ground-level testing. The operational health of the engine is achieved through the acquisition of spectrally resolved plume emissions and the subsequent identification of abnormal emission levels in the plume indicative of engine erosion or component failure. Eventually, OPAD, or a derivative of the technology, could find its way on to an actual space vehicle and provide in-flight engine condition monitoring. This technology step, however, will require miniaturized hardware capable of processing plume spectral data in real-time. An objective of OPAD condition monitoring is to determine how much of an element is present in the SSME plume. The basic premise is that by knowing the element and its concentration, this could be related back to the health of components within the engine. For example, an abnormal amount of silver in the plume might signify increased wear or deterioration of a particular bearing in the engine. Once an anomaly is identified, the engine could be shut down before catastrophic failure occurs. Currently, element concentrations in the plume are determined iteratively with the help of a non-linear computer

  20. Diagnosis of abnormal patterns in multivariate microclimate monitoring: a case study of an open-air archaeological site in Pompeii (Italy).

    PubMed

    Merello, Paloma; García-Diego, Fernando-Juan; Zarzo, Manuel

    2014-08-01

    Chemometrics has been applied successfully since the 1990s for the multivariate statistical control of industrial processes. A new area of interest for these tools is the microclimatic monitoring of cultural heritage. Sensors record climatic parameters over time and statistical data analysis is performed to obtain valuable information for preventive conservation. A case study of an open-air archaeological site is presented here. A set of 26 temperature and relative humidity data-loggers was installed in four rooms of Ariadne's house (Pompeii). If climatic values are recorded versus time at different positions, the resulting data structure is equivalent to records of physical parameters registered at several points of a continuous chemical process. However, there is an important difference in this case: continuous processes are controlled to reach a steady state, whilst open-air sites undergo tremendous fluctuations. Although data from continuous processes are usually column-centred prior to applying principal components analysis, it turned out that another pre-treatment (row-centred data) was more convenient for the interpretation of components and to identify abnormal patterns. The detection of typical trajectories was more straightforward by dividing the whole monitored period into several sub-periods, because the marked climatic fluctuations throughout the year affect the correlation structures. The proposed statistical methodology is of interest for the microclimatic monitoring of cultural heritage, particularly in the case of open-air or semi-confined archaeological sites. PMID:24814033

  1. Multivariate statistical monitoring as applied to clean-in-place (CIP) and steam-in-place (SIP) operations in biopharmaceutical manufacturing.

    PubMed

    Roy, Kevin; Undey, Cenk; Mistretta, Thomas; Naugle, Gregory; Sodhi, Manbir

    2014-01-01

    Multivariate statistical process monitoring (MSPM) is becoming increasingly utilized to further enhance process monitoring in the biopharmaceutical industry. MSPM can play a critical role when there are many measurements and these measurements are highly correlated, as is typical for many biopharmaceutical operations. Specifically, for processes such as cleaning-in-place (CIP) and steaming-in-place (SIP, also known as sterilization-in-place), control systems typically oversee the execution of the cycles, and verification of the outcome is based on offline assays. These offline assays add to delays and corrective actions may require additional setup times. Moreover, this conventional approach does not take interactive effects of process variables into account and cycle optimization opportunities as well as salient trends in the process may be missed. Therefore, more proactive and holistic online continued verification approaches are desirable. This article demonstrates the application of real-time MSPM to processes such as CIP and SIP with industrial examples. The proposed approach has significant potential for facilitating enhanced continuous verification, improved process understanding, abnormal situation detection, and predictive monitoring, as applied to CIP and SIP operations. PMID:24532460

  2. Condition Based Monitoring of Gas Turbine Combustion Components

    SciTech Connect

    Ulerich, Nancy; Kidane, Getnet; Spiegelberg, Christine; Tevs, Nikolai

    2012-09-30

    The objective of this program is to develop sensors that allow condition based monitoring of critical combustion parts of gas turbines. Siemens teamed with innovative, small companies that were developing sensor concepts that could monitor wearing and cracking of hot turbine parts. A magnetic crack monitoring sensor concept developed by JENTEK Sensors, Inc. was evaluated in laboratory tests. Designs for engine application were evaluated. The inability to develop a robust lead wire to transmit the signal long distances resulted in a discontinuation of this concept. An optical wear sensor concept proposed by K Sciences GP, LLC was tested in proof-of concept testing. The sensor concept depended, however, on optical fiber tips wearing with the loaded part. The fiber tip wear resulted in too much optical input variability; the sensor could not provide adequate stability for measurement. Siemens developed an alternative optical wear sensor approach that used a commercial PHILTEC, Inc. optical gap sensor with an optical spacer to remove fibers from the wearing surface. The gap sensor measured the length of the wearing spacer to follow loaded part wear. This optical wear sensor was developed to a Technology Readiness Level (TRL) of 5. It was validated in lab tests and installed on a floating transition seal in an F-Class gas turbine. Laboratory tests indicate that the concept can measure wear on loaded parts at temperatures up to 800{degrees}C with uncertainty of < 0.3 mm. Testing in an F-Class engine installation showed that the optical spacer wore with the wearing part. The electro-optics box located outside the engine enclosure survived the engine enclosure environment. The fiber optic cable and the optical spacer, however, both degraded after about 100 operating hours, impacting the signal analysis.

  3. A simplified scheme for induction motor condition monitoring

    NASA Astrophysics Data System (ADS)

    Rodríguez, Pedro Vicente Jover; Negrea, Marian; Arkkio, Antero

    2008-07-01

    This work proposes a general scheme to detect induction motor fault by monitoring the motor current. The scheme is based on signal processing (predictive filters) and soft computing technique (fuzzy logic). The predictive filter is used in order to separate the fundamental component from the harmonic components. Fuzzy logic is used to identify the motor state. Finite element method (FEM) is utilised to generate virtual data that allows to test the proposed technique and foresee the change in the current under different motor conditions. A simple and reliable method for the detection of stator winding failures based on the phase current amplitudes is implemented and tested. The layout has been proved in MATLAB/SIMULINK, with both data from FEM motor simulation program and real measurements. The proposed method has the ability to work with variable speed drives and avoids the detailed spectral analysis of the motor current. This work shows the feasibility of spotting broken rotor bars, eccentricities and inter-turn short-circuit by monitoring the motor currents.

  4. Embedded strain gauges for condition monitoring of silicone gaskets.

    PubMed

    Schotzko, Timo; Lang, Walter

    2014-01-01

    A miniaturized strain gauge with a thickness of 5 µm is molded into a silicone O-ring. This is a first step toward embedding sensors in gaskets for structural health monitoring. The signal of the integrated sensor exhibits a linear correlation with the contact pressure of the O-ring. This affords the opportunity to monitor the gasket condition during installation. Thus, damages caused by faulty assembly can be detected instantly, and early failures, with their associated consequences, can be prevented. Through the embedded strain gauge, the contact pressure applied to the gasket can be directly measured. Excessive pressure and incorrect positioning of the gasket can cause structural damage to the material of the gasket, which can lead to an early outage. A platinum strain gauge is fabricated on a thin polyimide layer and is contacted through gold connections. The measured resistance pressure response exhibits hysteresis for the first few strain cycles, followed by a linear behavior. The short-term impact of the embedded sensor on the stability of the gasket is investigated. Pull-tests with O-rings and test specimens have indicated that the integration of the miniaturized sensors has no negative impact on the stability in the short term. PMID:25014099

  5. Distributed acoustic fibre optic sensors for condition monitoring of pipelines

    NASA Astrophysics Data System (ADS)

    Hussels, Maria-Teresa; Chruscicki, Sebastian; Habib, Abdelkarim; Krebber, Katerina

    2016-05-01

    Industrial piping systems are particularly relevant to public safety and the continuous availability of infrastructure. However, condition monitoring systems based on many discrete sensors are generally not well-suited for widespread piping systems due to considerable installation effort, while use of distributed fibre-optic sensors would reduce this effort to a minimum. Specifically distributed acoustic sensing (DAS) is employed for detection of third-party threats and leaks in oil and gas pipelines in recent years and can in principle also be applied to industrial plants. Further possible detection routes amenable by DAS that could identify damage prior to emission of medium are subject of a current project at BAM, which aims at qualifying distributed fibre optic methods such as DAS as a means for spatially continuous monitoring of industrial piping systems. Here, first tests on a short pipe are presented, where optical fibres were applied directly to the surface. An artificial signal was used to define suitable parameters of the measurement system and compare different ways of applying the sensor.

  6. Embedded Strain Gauges for Condition Monitoring of Silicone Gaskets

    PubMed Central

    Schotzko, Timo; Lang, Walter

    2014-01-01

    A miniaturized strain gauge with a thickness of 5 µm is molded into a silicone O-ring. This is a first step toward embedding sensors in gaskets for structural health monitoring. The signal of the integrated sensor exhibits a linear correlation with the contact pressure of the O-ring. This affords the opportunity to monitor the gasket condition during installation. Thus, damages caused by faulty assembly can be detected instantly, and early failures, with their associated consequences, can be prevented. Through the embedded strain gauge, the contact pressure applied to the gasket can be directly measured. Excessive pressure and incorrect positioning of the gasket can cause structural damage to the material of the gasket, which can lead to an early outage. A platinum strain gauge is fabricated on a thin polyimide layer and is contacted through gold connections. The measured resistance pressure response exhibits hysteresis for the first few strain cycles, followed by a linear behavior. The short-term impact of the embedded sensor on the stability of the gasket is investigated. Pull-tests with O-rings and test specimens have indicated that the integration of the miniaturized sensors has no negative impact on the stability in the short term. PMID:25014099

  7. Diagnostic device for monitoring the technical condition of mechanical assemblies

    NASA Technical Reports Server (NTRS)

    Osovskiy, V. I.; Shergin, V. V.; Shumilin, V. I.

    1973-01-01

    An automatic diagnostic device for monitoring the condition of tractor transmission gears is described. The structural noise spectrum of the gearshift box and rear axle of the tractor were analyzed in a digital computer, by an algorithm based on the multiple correlation method. The optimum assembly of operating frequencies, by use of which the errors in measurement were minimized, was selected from the entire frequency spectrum. Selected frequencies are necessary for choosing the measurement range of the diagnostic device. It turned out that, to obtain a relative error of no more than 2%, it was sufficient to use two filters, vibrating only at the frequencies carrying the maximum data of the mechanical parameter being investigated. The measurement system consists of frequency-selection filters, amplifiers and quadratic detectors, at the outlets of which constant voltages are created, which are proportional to the signal level at the frequencies selected.

  8. New methods for the condition monitoring of level crossings

    NASA Astrophysics Data System (ADS)

    García Márquez, Fausto Pedro; Pedregal, Diego J.; Roberts, Clive

    2015-04-01

    Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a 'normal' or 'expected' shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque-Bera and Ljung-Box have been considered for testing the model.

  9. Condition monitoring of gearboxes using synchronously averaged electric motor signals

    NASA Astrophysics Data System (ADS)

    Ottewill, J. R.; Orkisz, M.

    2013-07-01

    Due to their prevalence in rotating machinery, the condition monitoring of gearboxes is extremely important in the minimization of potentially dangerous and expensive failures. Traditionally, gearbox condition monitoring has been conducted using measurements obtained from casing-mounted vibration transducers such as accelerometers. A well-established technique for analyzing such signals is the synchronous signal average, where vibration signals are synchronized to a measured angular position and then averaged from rotation to rotation. Driven, in part, by improvements in control methodologies based upon methods of estimating rotor speed and torque, induction machines are used increasingly in industry to drive rotating machinery. As a result, attempts have been made to diagnose defects using measured terminal currents and voltages. In this paper, the application of the synchronous signal averaging methodology to electric drive signals, by synchronizing stator current signals with a shaft position estimated from current and voltage measurements is proposed. Initially, a test-rig is introduced based on an induction motor driving a two-stage reduction gearbox which is loaded by a DC motor. It is shown that a defect seeded into the gearbox may be located using signals acquired from casing-mounted accelerometers and shaft mounted encoders. Using simple models of an induction motor and a gearbox, it is shown that it should be possible to observe gearbox defects in the measured stator current signal. A robust method of extracting the average speed of a machine from the current frequency spectrum, based on the location of sidebands of the power supply frequency due to rotor eccentricity, is presented. The synchronous signal averaging method is applied to the resulting estimations of rotor position and torsional vibration. Experimental results show that the method is extremely adept at locating gear tooth defects. Further results, considering different loads and different

  10. Microclimate monitoring in the Carcer Tullianum: temporal and spatial correlation and gradients evidenced by multivariate analysis; first campaign

    PubMed Central

    2012-01-01

    Too often microclimate studies in the field of cultural heritage are published without any or scarce information on sampling design, sensors (type, number, position) and instrument validation. Lacking of this fundamental information does not allow an open discussion in the scientific community. This work aims to be an invitation for a different approach. Three main parameters (temperature, humidity, luminance) were monitored in a selected part of a complex construction by an inexpensive self-assembled system along some horizontal and vertical vectors. All data was then processed and analyse by chemometric methods. Some measurements of oxygen, carbon monoxide and dioxide and pressure were also performed. Correlation of some indoor and outdoor data was shown for temperature and humidity. In case of outdoor changes the indoor environment reacted with a certain delay which is position-dependent and more evident for humidity data. The two observed rooms (Carcer and Tullianum) behave differently and the hypogean one is less influenced by the outdoor environment. Instrument validation before and after the campaign, allows to consider detected variations as significant. The fundamental importance of Sampling Design and of instrument validation before and after the monitoring campaign was enhanced. The choice of two main and two minor vectors allowed detection of different behaviour for the two rooms, also permitting to detect for both rooms a trend towards a spontaneous microclimate necessary for a conservation project. In the next campaign we will focus on the choice of the best sampling frequency to use more sophisticated statistical methods. PMID:22594436

  11. The Performance of Cross-Validation Indices Used to Select among Competing Covariance Structure Models under Multivariate Nonnormality Conditions

    ERIC Educational Resources Information Center

    Whittaker, Tiffany A.; Stapleton, Laura M.

    2006-01-01

    Cudeck and Browne (1983) proposed using cross-validation as a model selection technique in structural equation modeling. The purpose of this study is to examine the performance of eight cross-validation indices under conditions not yet examined in the relevant literature, such as nonnormality and cross-validation design. The performance of each…

  12. A modern diagnostic approach for automobile systems condition monitoring

    NASA Astrophysics Data System (ADS)

    Selig, M.; Shi, Z.; Ball, A.; Schmidt, K.

    2012-05-01

    An important topic in automotive research and development is the area of active and passive safety systems. In general, it is grouped in active safety systems to prevent accidents and passive systems to reduce the impact of a crash. An example for an active system is ABS while a seat belt tensioner represents the group of passive systems. Current developments in the automotive industry try to link active with passive system components to enable a complete event sequence, beginning with the warning of the driver about a critical situation till the automatic emergency call after an accident. The cross-linking has an impact on the current diagnostic approach, which is described in this paper. Therefore, this contribution introduces a new diagnostic approach for automotive mechatronic systems. The concept is based on monitoring the messages which are exchanged via the automotive communication systems, e.g. the CAN bus. According to the authors' assumption, the messages on the bus are changing between faultless and faulty vehicle condition. The transmitted messages of the sensors and control units are different depending on the condition of the car. First experiments are carried and in addition, the hardware design of a suitable diagnostic interface is presented. Finally, first results will be presented and discussed.

  13. Application of near-infrared spectroscopy combined with multivariate analysis in monitoring of crude heparin purification process

    NASA Astrophysics Data System (ADS)

    Zang, Hengchang; Wang, Jinfeng; Li, Lian; Zhang, Hui; Jiang, Wei; Wang, Fengshan

    2013-05-01

    Ion-exchange chromatography is a widely used purification technology in the heparin manufacturing process. To improve the efficiency and understand the process directly, a rapid and equally precise method needs to be developed to measure heparin concentration in chromatography process. Here, two robust partial least squares regression (PLS-R) models were established for quantification of heparin based on the near-infrared (NIR) spectroscopy with 80 samples of adsorption process and 76 samples of elution process. Several variables selection algorithms, including correlation coefficient method, successive projection algorithm (SPA) and interval partial least squares (iPLSs), were performed to remove non-informative variables. The results showed that the correlation coefficient of validation (Rp) and the residual predictive deviation (RPD) corresponded to 0.957 and 3.4472 for adsorption process, 0.968 and 3.9849 for elution process, respectively. The approach was found considerable potential for real-time monitoring the heparin concentration of chromatography process.

  14. MONITORING SPENT NUCLEAR FUEL REPROCESSING CONDITIONS NON-DESTRUCTIVELY AND IN NEAR-REAL-TIME USING THE MULTI-ISOTOPE PROCESS (MIP) MONITOR

    SciTech Connect

    Orton, Christopher R.; Fraga, Carlos G.; Douglas, Matthew; Christensen, Richard; Schwantes, Jon M.

    2010-05-07

    Researchers from Pacific Northwest National Laboratory and The Ohio State University are working to develop a system for monitoring spent nuclear fuel reprocessing facilities on-line, nondestructively, and in near-real-time. This method, known as the Multi-Isotope Process (MIP) Monitor, is based upon the measurement of distribution patterns of a suite of indicator (radioactive) isotopes present within product and waste streams of a nuclear reprocessing facility. Signatures from these indicator isotopes are monitored on-line by gamma spectrometry and compared, in near-real-time, to patterns representing "normal" process conditions using multivariate pattern recognition software. By targeting gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, high-resolution gamma detectors that may be easily deployed throughout an existing facility. In addition, utilization of a suite of radio-elements, including ones with multiple oxidation states, increases the likelihood that attempts to divert material via process manipulation would be detected. Proof-of-principle modeling exercises simulating changes in acid strength have been completed and the results are promising. Laboratory testing is currently under way and significant results are available. Recent experimental results, along with an overview of the method are presented.

  15. Increasing Potlife of Hall-Héroult Reduction Cells Through Multivariate On-Line Monitoring of Preheating, Start-Up, and Early Operation

    NASA Astrophysics Data System (ADS)

    Tessier, Jayson; Duchesne, Carl; Tarcy, Gary P.; Gauthier, Claude; Dufour, Gilles

    2010-06-01

    Aluminum is produced inside metallurgical reactors known as pots that are replaced at the end of their service life. New pots are preheated, started, and then enter a period known as early operation in which different control strategies are used before entering regular operation. It is known that how preheating, start-up, and early operation are performed can damage a well-designed pot and lead to a shorter service life. However, the impact of these phases with respect to potlife is not well documented quantitatively. In this article, multivariate statistical analysis techniques are used to investigate the impact of pot-to-pot variations during the three phases. A partial least squares regression model is first proposed for predicting potlife, within an error of 90 days, using process data gathered until the end of early operation. This model is also used to identify those variables having the greatest influence on potlife. Finally, multivariate statistical process control charts are proposed to monitor the three steps efficiently. These charts have a low false-alarm rate and can help find the root cause of abnormal operation occurring during the early phases. A few examples are used to illustrate how operators and engineers could use the charts to maintain consistent early operation and help improve mean potlife. Nomenclature: In this article, bold characters are used to identify vectors (bold lowercase), matrices (bold capital), and three-dimensional arrays (bold, underlined capital). Lowercase italics letters are used to define indices. Al—Aluminium; Al2O3—Alumina; C—Carbon; CO2—Carbon dioxide; kA—kilo-Amperes; Na—Sodium; Na3AlF6—Cryolite; V—Volt.

  16. Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.

    PubMed

    Ruiz, Magda; Sin, Gürkan; Berjaga, Xavier; Colprim, Jesús; Puig, Sebastià; Colomer, Joan

    2011-01-01

    The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation. PMID:22335109

  17. Stability conditions of the Vistula Valley attained by a multivariate approach - a case study from the Warsaw Southern Ring Road

    NASA Astrophysics Data System (ADS)

    Kaczmarek, Łukasz; Dobak, Paweł

    2015-12-01

    Localised landslide activity has been observed in the area of the plateau slope analysed, in the vicinity of the planned Warsaw Southern Ring Road. Using calculation models quantitative and qualitative evaluations of the impact of natural and anthropogenic load factors on slope stability (and hence, safety) are made. The present paper defines six stages of slope stability analysis, leading to an indication of optimum slope design in relation to the development planned. The proposed procedure produces a ranking of factors that affect slope stability. In the engineering geological conditions under consideration, the greatest factors impacting degradation and failure of slope stability are changes in soil strength due to local, periodic yielding and the presence of dynamic loads generated by intensification of road traffic. Calculation models were used to assess the impact of destabilisation factors and to obtain mutual equivalence with 3D-visualisation relations. Based on this methodology, various scenarios dedicated to specific engineering geological conditions can be developed and rapid stability evaluations of changing slope loads can be performed.

  18. Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship.

    PubMed

    Liu, Yingchun; Sun, Guoxiang; Wang, Yan; Yang, Lanping; Yang, Fangliang

    2015-06-01

    Micellar electrokinetic chromatography fingerprinting combined with quantification was successfully developed and applied to monitor the quality consistency of Weibizhi tablets, which is a classical compound preparation used to treat gastric ulcers. A background electrolyte composed of 57 mmol/L sodium borate, 21 mmol/L sodium dodecylsulfate and 100 mmol/L sodium hydroxide was used to separate compounds. To optimize capillary electrophoresis conditions, multivariate statistical analyses were applied. First, the most important factors influencing sample electrophoretic behavior were identified as background electrolyte concentrations. Then, a Box-Benhnken design response surface strategy using resolution index RF as an integrated response was set up to correlate factors with response. RF reflects the effective signal amount, resolution, and signal homogenization in an electropherogram, thus, it was regarded as an excellent indicator. In fingerprint assessments, simple quantified ratio fingerprint method was established for comprehensive quality discrimination of traditional Chinese medicines/herbal medicines from qualitative and quantitative perspectives, by which the quality of 27 samples from the same manufacturer were well differentiated. In addition, the fingerprint-efficacy relationship between fingerprints and antioxidant activities was established using partial least squares regression, which provided important medicinal efficacy information for quality control. The present study offered an efficient means for monitoring Weibizhi tablet quality consistency. PMID:25867134

  19. A knowledge based expert system for condition monitoring

    SciTech Connect

    Selkirk, C.G.; Roberge, P.R.; Fisher, G.F.; Yeung, K.K.

    1994-12-31

    Condition monitoring (CM) is the focus of many maintenance philosophies around the world today. In the Canadian Forces (CF), CM has played an important role in the maintenance of aircraft systems since the introduction of spectrometric oil analysis (SOAP) over twenty years ago. Other techniques in use in the CF today include vibration analysis (VA), ferrography, and filter debris analysis (FDA). To improve the usefulness and utility gained from these CM techniques, work is currently underway to incorporate expert systems into them. An expert system for FDA is being developed which will aid filter debris analysts in identifying wear debris and wear level trends, and which will provide the analyst with reference examples in an attempt to standardize results. Once completed, this knowledge based expert system will provide a blueprint from which other CM expert systems can be created. Amalgamating these specific systems into a broad based global system will provide the CM analyst with a tool that will be able to correlate data and results from each of the techniques, thereby increasing the utility of each individual method of analysis. This paper will introduce FDA and then outline the development of the FDA expert system and future applications.

  20. Structural health monitoring methodology for aircraft condition-based maintenance

    NASA Astrophysics Data System (ADS)

    Saniger, Jordi; Reithler, Livier; Guedra-Degeorges, Didier; Takeda, Nobuo; Dupuis, Jean Pierre

    2001-06-01

    Reducing maintenance costs while keeping a constant level of safety is a major issue for Air Forces and airlines. The long term perspective is to implement condition based maintenance to guarantee a constant safety level while decreasing maintenance costs. On this purpose, the development of a generalized Structural Health Monitoring System (SHMS) is needed. The objective of such a system is to localize the damages and to assess their severity, with enough accuracy to allow low cost corrective actions. The present paper describes a SHMS based on acoustic emission technology. This choice was driven by its reliability and wide use in the aerospace industry. The described SHMS uses a new learning methodology which relies on the generation of artificial acoustic emission events on the structure and an acoustic emission sensor network. The calibrated acoustic emission events picked up by the sensors constitute the knowledge set that the system relies on. With this methodology, the anisotropy of composite structures is taken into account, thus avoiding the major cause of errors of classical localization methods. Moreover, it is adaptive to different structures as it does not rely on any particular model but on measured data. The acquired data is processed and the event's location and corrected amplitude are computed. The methodology has been demonstrated and experimental tests on elementary samples presented a degree of accuracy of 1cm.

  1. Comparison of a Multimetric Index and a Multivariate Predictive Model for Assessing the Biological Condition of Kentucky Streams

    NASA Astrophysics Data System (ADS)

    Pond, G. J.

    2005-05-01

    There is still debate on the strength of various data analysis tools for assessing biological condition in streams. This study compared two popular assessment approaches (multimetric index and RIVPACS-type O/E model) using macroinvertebrates from Kentucky streams. Data from 557 targeted and randomly selected sites (212 reference, 345 non-reference) sampled between 2000 and 2004 were used in this analysis. The Kentucky Macroinvertebrate Bioassessment Index (MBI) combines seven metrics (total generic richness, EPT generic richness, modified HBI, %Ephemeroptera, %EPT minus Cheumatopsyche, %midges+worms, and %clingers) that are scored by standardizing to the 95th or 5th percentile of the reference distribution and averaged. For comparison, three separate genus-level RIVPACS-type models were constructed (high-, low-, and mixed gradient streams) using four predictive variables (area, latitude, longitude, and week number) and taxa from reference sites. All 3 models preformed well but the low gradient model had the lowest precision. Assessments of non-reference sites based on MBI and O/E scores yielded similar results in terms of discrimination efficiency but the model based on mixed-gradient streams was the least sensitive. Using a subset of data from 84 headwater streams in the Appalachian region, MBI and O/E scores responded almost identically to stressors such as conductivity and habitat degradation.

  2. An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

    NASA Technical Reports Server (NTRS)

    Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

    2013-01-01

    In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

  3. An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

    NASA Technical Reports Server (NTRS)

    Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

    2013-01-01

    In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter. In order to increase overall robustness, the vehicle also has an alternate method of triggering the drogue parachute deployment based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this velocity-based trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers excellent performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

  4. An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

    NASA Technical Reports Server (NTRS)

    Smith, Kelly; Gay, Robert; Stachowiak, Susan

    2013-01-01

    In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

  5. 10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive... a minimum— (a) Each licensee shall monitor occupational exposure to radiation from licensed...

  6. 10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive... a minimum— (a) Each licensee shall monitor occupational exposure to radiation from licensed...

  7. 10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive... a minimum— (a) Each licensee shall monitor occupational exposure to radiation from licensed...

  8. 10 CFR 20.1502 - Conditions requiring individual monitoring of external and internal occupational dose.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... PROTECTION AGAINST RADIATION Surveys and Monitoring § 20.1502 Conditions requiring individual monitoring of external and internal occupational dose. Each licensee shall monitor exposures to radiation and radioactive... a minimum— (a) Each licensee shall monitor occupational exposure to radiation from licensed...

  9. Investigation of Various Condition Monitoring Techniques Based on a Damaged Wind Turbine Gearbox

    SciTech Connect

    Sheng, S.

    2011-10-01

    This paper is a continuation of a 2009 paper presented at the 7th International Workshop on Structural Health Monitoring that described various wind turbine condition-monitoring techniques. This paper presents the results obtained by various condition- monitoring techniques from a damaged Gearbox Reliability Collaborative test gearbox.

  10. Multivariate normality

    NASA Technical Reports Server (NTRS)

    Crutcher, H. L.; Falls, L. W.

    1976-01-01

    Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

  11. MONITORING STREAM CONDITION IN THE WESTERN UNITED STATES

    EPA Science Inventory


    The U.S. Environmental Protection Agency Environmental Monitoring and Assessment Program (EMAP) is a national research program to develop the tools necessary to monitor and assess the- status and trends of ecological resources. EMAP's goal is to develop the scientific underst...

  12. Monitoring and Reporting Hospital-Acquired Conditions: A Federalist Approach

    PubMed Central

    West, Nathan; Eng, Terry

    2015-01-01

    Background Serious adverse events that occur in hospitals rank as a leading cause of preventable death in the United States. Many states operate reporting systems to monitor and publicly report serious adverse events, a subset that falls under Medicare’s Hospital-Acquired Conditions (HACs). Purpose(s) Identify and describe state efforts, and the supporting role of federal initiatives, to track and report HACs and other serious adverse events. Data Sources Document review of state and federal reports, databases, and policies for HACs and other serious adverse events; conduct semi-structured telephone interviews with state health department officials and directors of patient safety organizations. Results Thirty-two states and the District of Columbia (D.C.) track at least one Medicare HAC. Five states collect nearly all ten Medicare HACs (9–10). Eighteen states and D.C. track events through both a state-based reporting system and the Centers for Disease Control National Healthcare Safety Network (NHSN) for health-care associated infections (HAI). For serious adverse events, most states either partially or fully adopted the National Quality Forum’s Serious Reportable Events. For HAIs, thirty states and D.C. mandate reporting through NHSN. States interviewed reported that Medicare’s choice of HACs for nonpayment had at least a partial influence on which serious adverse events required reporting. Conclusions Many states use the collected data on HACs and other events for quality improvement initiatives and to provide greater transparency through public reporting. More work and research is needed to develop a national reporting system template that has standard definitions, methodology, and reporting. PMID:25584196

  13. Raman and surface-enhanced Raman spectroscopy for renal condition monitoring

    NASA Astrophysics Data System (ADS)

    Li, Jingting; Li, Ming; Du, Yong; Santos, Greggy M.; Mohan, Chandra; Shih, Wei-Chuan

    2016-03-01

    Non- and minimally-invasive techniques can provide advantages in the monitoring and clinical diagnostics in renal diseases. Although renal biopsy may be useful in establishing diagnosis in several diseases, it is an invasive approach and impractical for longitudinal disease monitoring. To address this unmet need, we have developed two techniques based on Raman spectroscopy. First, we have investigated the potential of diagnosing and staging nephritis by analyzing kidney tissue Raman spectra using multivariate techniques. Secondly, we have developed a urine creatinine sensor based on surface-enhanced Raman spectroscopy with performance near commercial assays which require relatively laborious sample preparation and longer time.

  14. BIRD COMMUNITIES AND HABITAT AS ECOLOGICAL INDICATORS OF FOREST CONDITION IN REGIONAL MONITORING

    EPA Science Inventory

    Ecological indicators for long-term monitoring programs are needed to detect and assess changing environmental conditions, We developed and tested community-level environmental indicators for monitoring forest bird populations and associated habitat. We surveyed 197 sampling plo...

  15. Wetlands monitoring - hydrological conditions and water quality in selected transects of Biebrza National Park.

    NASA Astrophysics Data System (ADS)

    Stelmaszczyk, Mateusz; Okruszko, Tomasz

    2010-05-01

    . Studied locations were covered mainly by Magnocaricion vegetation (e.g. Caricetum gracilis and Caricetum elatae), Molinio-Arrhenatheretea vegetation (Molinietum caeruleae), and Scheuchzerio-Caricetea nigrae vegetation (e.g. Caricetum lasiocarpae). In presented work authors show results of water quality measurements and monitoring of hydrological conditions, characterized by changes of groundwater table, period and size of inundation. During six years long monitoring period (2004 - 2009 hydrological years) there were observed high diversification of groundwater and surface water levels among locations. They fluctuate in some places from very low groundwater levels, observed in late summer and in early autumn (over 1 m beneath the ground), to levels reaching surface of the ground or laying nearly below it, occurring in winter and spring. There are also places where quite high inundations in winter and spring are observed. Collected chemical and hydrological data were statistically analyzed using STATISTICA 8 software with a use of one of the multivariate analysis - Principal Component Analysis (PCA) method. Owing to the usage of PCA analysis it was possible to define most important parameters characterizing habitats were occurs selected vegetation. The impact of hydrological conditions (presented as a main factor) on forming particular wetland plant communities can be discussed. Authors determine that some other factors (e.g. management) can be more responsible for occurrence of particular plant communities and their sustaining in good status in specific locations.

  16. Low-cost micro condition monitoring system based on LabVIEW and SQL server

    NASA Astrophysics Data System (ADS)

    Jia, Zhizhou; Guo, Yu; Fan, Yajun

    2013-03-01

    Due to most of the existing condition monitoring systems have a rather complicated structure and the high cost makes even big companies can only afford on a few key equipments, a developing scheme of low-cost micro condition monitoring system based on LabVIEW and SQL Server is proposed in this paper. The low-cost micro condition monitoring system can realize the effective monitoring to general machinery by full taking the advantages of LabVIEW and SQL Server respectively. The system supplements the existing condition monitoring systems to some extent. It affords good applicability and expanding ability, which make it suitable for the equipment management of enterprises for general equipment condition monitoring and health maintenance.

  17. Machine condition monitoring using neural networks and the likelihood function

    SciTech Connect

    Vilim, R.B.; Garcia, H.E.; Chen, F.W.

    1997-09-01

    A model-based technique incorporating neural networks has been developed for process monitoring. The technique is intended for processes where the uncertainty in the reference model is larger than desired but where process measurements providing additional information about the behavior of the system are available. This data is used to reduce the uncertainty of the model. The technique has been implemented in a real-time system for monitoring operational changes of mechanical equipment for use in predictive maintenance applications. Tests on a peristaltic pump were conducted and demonstrate the advantages of the proposed technique.

  18. Wireless sensor network for monitoring soil moisture and weather conditions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A wireless sensor network (WSN) was developed and deployed in three fields to monitor soil water status and collect weather data for irrigation scheduling. The WSN consists of soil-water sensors, weather sensors, wireless data loggers, and a wireless modem. Soil-water sensors were installed at three...

  19. VARIOGRAPHY AND CONDITIONAL SEQUENTIAL SIMULATION: NEW TOOLS FOR ECOLOGICAL MONITORING

    EPA Science Inventory

    The Superfund reauthorization Act requires an ecological impact statement as part of each site assessment. his is difficult because of the hierarchical multiple dimensionality of ecosystems and becaus of the limited time and resources for the site's monitoring and evaluation. he ...

  20. 40 CFR 141.625 - Conditions requiring increased monitoring.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ...) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS Stage 2 Disinfection Byproducts... monitoring to dual sample sets once per quarter (taken every 90 days) at all locations if a TTHM sample is >0.080 mg/L or a HAA5 sample is >0.060 mg/L at any location. (b) You are in violation of the MCL when...

  1. An online technique for condition monitoring the induction generators used in wind and marine turbines

    NASA Astrophysics Data System (ADS)

    Yang, Wenxian; Tavner, P. J.; Court, R.

    2013-07-01

    Induction generators have been successfully applied to a variety of industries. However, their operation and maintenance in renewable wind and marine energy industries still face challenges due to harsh environments, limited access to site and relevant reliability issues. Hence, further enhancing their condition monitoring is regarded as one of the essential measures for improving their availability. To date, much effort has been made to monitor induction motors, which can be equally applied to monitoring induction generators. However, the achieved techniques still have constrains in particular when dealing with the condition monitoring problems in wind and marine turbine generators. For example, physical measurements of partial discharge, noise and temperature have been widely applied to monitoring induction machinery. They are simple and cost-effective, but unable to be used for fault diagnosis. The spectral analysis of vibration and stator current signals is also a mature technique popularly used in motor/generator condition monitoring practice. However, it often requires sufficient expertise for data interpretation, and significant pre-knowledge about the machines and their components. In particular in renewable wind and marine industries, the condition monitoring results are usually coupled with load variations, which further increases the difficulty of obtaining a reliable condition monitoring result. In view of these issues, a new condition monitoring technique is developed in this paper dedicated for wind and marine turbine generators. It is simple, informative and less load-dependent thus more reliable to deal with the online motor/generator condition monitoring problems under varying loading conditions. The technique has been verified through both simulated and practical experiments. It has been shown that with the aid of the proposed technique, not only the electrical faults but also the shaft unbalance occurring in the generator become detectable

  2. Monitor weather conditions for cloud seeding control. [Colorado River Basin

    NASA Technical Reports Server (NTRS)

    Kahan, A. M. (Principal Investigator)

    1973-01-01

    The author has identified the following significant results. The near real-time DCS platform data transfer to the time-share compare is a working reality. Six stations are now being automatically monitored and displayed with a system delay of 3 to 8 hours from time of data transmission to time of data accessibility on the computer. The DCS platform system has proven itself a valuable tool for near real-time monitoring of mountain precipitation. Data from Wolf Creek Pass were an important input in making the decision when to suspend seeding operations to avoid exceeding suspension criteria in that area. The DCS platforms, as deployed in this investigation, have proven themselves to be reliable weather resistant systems for winter mountain environments in the southern Colorado mountains.

  3. Chiller condition monitoring using topological case-based modeling

    SciTech Connect

    Tsutsui, Hiroaki; Kamimura, Kazuyuki

    1996-11-01

    To increase energy efficiency and economy, commercial building projects now often utilize centralized, shared sources of heat such as district heating and cooling (DHC) systems. To maintain efficiency, precise monitoring and scheduling of maintenance for chillers and heat pumps is essential. Low-performance operation results in energy loss, while unnecessary maintenance is expensive and wasteful. Plant supervisors are responsible for scheduling and supervising maintenance. Modeling systems that assist in analyzing system deterioration are of great benefit for these tasks. Topological case-based modeling (TCBM) (Tsutsui et al. 1993; Tsutsui 1995) is an effective tool for chiller performance deterioration monitoring. This paper describes TCBM and its application to this task using recorded historical performance data.

  4. 77 FR 24228 - Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-04-23

    ... COMMISSION Condition Monitoring Techniques for Electric Cables Used in Nuclear Power Plants AGENCY: Nuclear... Techniques for Electric Cables Used in Nuclear Power Plants.'' This guide describes techniques that the staff of the NRC considers acceptable for condition monitoring of electric cables for nuclear power...

  5. Development of GUI Type On-Line Condition Monitoring Program for a Turboprop Engine Using Labview

    NASA Astrophysics Data System (ADS)

    Kong, Changduk; Kim, Keonwoo

    2011-12-01

    Recently, an aero gas turbine health monitoring system has been developed for precaution and maintenance action against faults or performance degradations of the advanced propulsion system which occurs in severe environments such as high altitude, foreign object damage particles, hot and heavy rain and snowy atmospheric conditions. However to establish this health monitoring system, the online condition monitoring program is firstly required, and the program must monitor the engine performance trend through comparison between measured engine performance data and base performance results calculated by base engine performance model. This work aims to develop a GUI type on-line condition monitoring program for the PT6A-67 turboprop engine of a high altitude and long endurance operation UAV using LabVIEW. The base engine performance of the on-line condition monitoring program is simulated using component maps inversely generated from the limited performance deck data provided by engine manufacturer. The base engine performance simulation program is evaluated because analysis results by this program agree well with the performance deck data. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between clean engine performance results calculated by the base performance simulation program and measured engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed online monitoring system. For user friendly purpose, all monitoring program are coded by LabVIEW, and monitoring examples are demonstrated using the proposed GUI type on-condition monitoring program.

  6. Optical methods for hydrogen degassing monitoring in urban conditions

    NASA Astrophysics Data System (ADS)

    Timchenko, E. V.; Timchenko, P. E.; Zherdeva, L. A.; Tregub, N. V.; Selezneva, E. A.; Yakovlev, V. N.

    2015-12-01

    Results of a study of variations in optical parameters of bioindicators that grow in the regions of hydrogen degassing in Samara are presented. Raman spectroscopy and confocal fluorescence microscopy were used as the main methods of the study. Features of Raman spectra of plants that grow in zones with presence/ absence of deep hydrogen emissions have been ascertained. The main variations have been recorded at wavenumbers of 1380, 1522, 1547, and 1600 cm-1, which are responsible for stretching vibrations in lignin and β-carotene and chlorophyll a and cellulose in plant leaves. Confocal fluorescence microscopy showed an increase in the chloroplasts in leaves of plants which grow at hydrogen degassing territories. An optical coefficient was introduced, on the basis of which the Samara region was monitored.

  7. PROCESS OF SELECTING INDICATORS FOR MONITORING CONDITIONS OF RANGELAND HEALTH

    EPA Science Inventory

    This paper reports on a process for selecting a suite of indicators that, in combination, can be useful in assessing the ecological conditions of rangelands. onceptual models that depict the structural and functional properties of ecological processes were used to show the linkag...

  8. Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zhang, Chunlin; Zhang, Xiaoyan; Zi, Yanyang; He, Shuilong; Yang, Zhe

    2015-03-01

    Satellite communication antennas are key devices of a measurement ship to support voice, data, fax and video integration services. Condition monitoring of mechanical equipment from the vibration measurement data is significant for guaranteeing safe operation and avoiding the unscheduled breakdown. So, condition monitoring system for ship-based satellite communication antennas is designed and developed. Planetary gearboxes play an important role in the transmission train of satellite communication antenna. However, condition monitoring of planetary gearbox still faces challenges due to complexity and weak condition feature. This paper provides a possibility for planetary gearbox condition monitoring by proposing ensemble a multiwavelet analysis method. Benefit from the property on multi-resolution analysis and the multiple wavelet basis functions, multiwavelet has the advantage over characterizing the non-stationary signal. In order to realize the accurate detection of the condition feature and multi-resolution analysis in the whole frequency band, adaptive multiwavelet basis function is constructed via increasing multiplicity and then vibration signal is processed by the ensemble multiwavelet transform. Finally, normalized ensemble multiwavelet transform information entropy is computed to describe the condition of planetary gearbox. The effectiveness of proposed method is first validated through condition monitoring of experimental planetary gearbox. Then this method is used for planetary gearbox condition monitoring of ship-based satellite communication antennas and the results support its feasibility.

  9. A remote condition monitoring system for wind-turbine based DG systems

    NASA Astrophysics Data System (ADS)

    Ma, X.; Wang, G.; Cross, P.; Zhang, X.

    2012-05-01

    In this paper, a remote condition monitoring system is proposed, which fundamentally consists of real-time monitoring modules on the plant side, a remote support centre and the communications between them. The paper addresses some of the key issues related on the monitoring system, including i) the implementation and configuration of a VPN connection, ii) an effective database system to be able to handle huge amount of monitoring data, and iii) efficient data mining techniques to convert raw data into useful information for plant assessment. The preliminary results have demonstrated that the proposed system is practically feasible and can be deployed to monitor the emerging new energy generation systems.

  10. Health Monitoring and Management for Manufacturing Workers in Adverse Working Conditions.

    PubMed

    Xu, Xiaoya; Zhong, Miao; Wan, Jiafu; Yi, Minglun; Gao, Tiancheng

    2016-10-01

    In adverse working conditions, environmental parameters such as metallic dust, noise, and environmental temperature, directly affect the health condition of manufacturing workers. It is therefore important to implement health monitoring and management based on important physiological parameters (e.g., heart rate, blood pressure, and body temperature). In recent years, new technologies, such as body area networks, cloud computing, and smart clothing, have allowed the improvement of the quality of services. In this article, we first give five-layer architecture for health monitoring and management of manufacturing workers. Then, we analyze the system implementation process, including environmental data processing, physical condition monitoring and system services and management, and present the corresponding algorithms. Finally, we carry out an evaluation and analysis from the perspective of insurance and compensation for manufacturing workers in adverse working conditions. The proposed scheme will contribute to the improvement of workplace conditions, realize health monitoring and management, and protect the interests of manufacturing workers. PMID:27624491

  11. A NATIONAL PROGRAM FOR MONITORING STREAM CONDITION IN THE WESTERN UNITED STATES

    EPA Science Inventory


    The U.S. Environmental Protection Agency recently initiated a four-year survey of streams in the Western United States as a component of the Environmental Monitoring and Assessment Program (EMAP). EMAP is developing indicators to monitor and assess the condition of ecological...

  12. Monitoring network-design influence on assessment of ecological condition in wadeable streams

    EPA Science Inventory

    We investigated outcomes of three monitoring networks for assessing ecological character and condition of wadeable streams in the Waikato region, New Zealand. Sites were selected 1) based on a professional judgment network, 2) within categories of stream and watershed characteris...

  13. Operational-Condition-Independent Criteria Dedicated to Monitoring Wind Turbine Generators: Preprint

    SciTech Connect

    Yang, W.; Sheng, S.; Court, R.

    2012-08-01

    To date the existing wind turbine condition monitoring technologies and commercially available systems have not been fully accepted for improving wind turbine availability and reducing their operation and maintenance costs. One of the main reasons is that wind turbines are subject to constantly varying loads and operate at variable rotational speeds. As a consequence, the influences of turbine faults and the effects of varying load and speed are coupled together in wind turbine condition monitoring signals. So, there is an urgent need to either introduce some operational condition de-coupling procedures into the current wind turbine condition monitoring techniques or develop a new operational condition independent wind turbine condition monitoring technique to maintain high turbine availability and achieve the expected economic benefits from wind. The purpose of this paper is to develop such a technique. In the paper, three operational condition independent criteria are developed dedicated for monitoring the operation and health condition of wind turbine generators. All proposed criteria have been tested through both simulated and practical experiments. The experiments have shown that these criteria provide a solution for detecting both mechanical and electrical faults occurring in wind turbine generators.

  14. Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

    NASA Astrophysics Data System (ADS)

    Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

    2016-01-01

    The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

  15. Comparison of multispectral remote-sensing techniques for monitoring subsurface drain conditions. [Imperial Valley, California

    NASA Technical Reports Server (NTRS)

    Goettelman, R. C.; Grass, L. B.; Millard, J. P.; Nixon, P. R.

    1983-01-01

    The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.

  16. Application of Condition-Based Monitoring Techniques for Remote Monitoring of a Simulated Gas Centrifuge Enrichment Plant

    SciTech Connect

    Hooper, David A; Henkel, James J; Whitaker, Michael

    2012-01-01

    This paper presents research into the adaptation of monitoring techniques from maintainability and reliability (M&R) engineering for remote unattended monitoring of gas centrifuge enrichment plants (GCEPs) for international safeguards. Two categories of techniques are discussed: the sequential probability ratio test (SPRT) for diagnostic monitoring, and sequential Monte Carlo (SMC or, more commonly, particle filtering ) for prognostic monitoring. Development and testing of the application of condition-based monitoring (CBM) techniques was performed on the Oak Ridge Mock Feed and Withdrawal (F&W) facility as a proof of principle. CBM techniques have been extensively developed for M&R assessment of physical processes, such as manufacturing and power plants. These techniques are normally used to locate and diagnose the effects of mechanical degradation of equipment to aid in planning of maintenance and repair cycles. In a safeguards environment, however, the goal is not to identify mechanical deterioration, but to detect and diagnose (and potentially predict) attempts to circumvent normal, declared facility operations, such as through protracted diversion of enriched material. The CBM techniques are first explained from the traditional perspective of maintenance and reliability engineering. The adaptation of CBM techniques to inspector monitoring is then discussed, focusing on the unique challenges of decision-based effects rather than equipment degradation effects. These techniques are then applied to the Oak Ridge Mock F&W facility a water-based physical simulation of a material feed and withdrawal process used at enrichment plants that is used to develop and test online monitoring techniques for fully information-driven safeguards of GCEPs. Advantages and limitations of the CBM approach to online monitoring are discussed, as well as the potential challenges of adapting CBM concepts to safeguards applications.

  17. A New Experimental Method for in Situ Corrosion Monitoring Under Alternate Wet-Dry Conditions

    PubMed Central

    Fu, Xinxin; Dong, Junhua; Han, Enhou; Ke, Wei

    2009-01-01

    A new experimental method was applied in in situ corrosion monitoring of mild steel Q235 under alternate wet-dry conditions. The thickness of the electrolyte film during the wet cycle was monitored by a high-precision balance with a sensibility of 0.1 mg. At the same time, an electrochemical impedance technique was employed to study the effect of film thickness on corrosion rates. Experimental results showed that there was a critical electrolyte film condition for which the corrosion rate reached a maximum during wet-dry cycles. For the substrate, the critical condition could be described by a film thickness of about 17 μm. For the rusted specimen, the critical condition could be described by an electrolyte amount of about 0.038 g, which is equivalent to a film thickness of 38 μm. This monitoring system was very useful for studying atmospheric corrosion of metals covered by corrosion products. PMID:22303180

  18. A new experimental method for in situ corrosion monitoring under alternate wet-dry conditions.

    PubMed

    Fu, Xinxin; Dong, Junhua; Han, Enhou; Ke, Wei

    2009-01-01

    A new experimental method was applied in in situ corrosion monitoring of mild steel Q235 under alternate wet-dry conditions. The thickness of the electrolyte film during the wet cycle was monitored by a high-precision balance with a sensibility of 0.1 mg. At the same time, an electrochemical impedance technique was employed to study the effect of film thickness on corrosion rates. Experimental results showed that there was a critical electrolyte film condition for which the corrosion rate reached a maximum during wet-dry cycles. For the substrate, the critical condition could be described by a film thickness of about 17 μm. For the rusted specimen, the critical condition could be described by an electrolyte amount of about 0.038 g, which is equivalent to a film thickness of 38 μm. This monitoring system was very useful for studying atmospheric corrosion of metals covered by corrosion products. PMID:22303180

  19. Monitoring cutting tool operation and condition with a magnetoelastic rate of change of torque sensor

    SciTech Connect

    Garshelis, Ivan J.; Kari, Ryan J.; Tollens, Stijn P. L.; Cuseo, James M.

    2008-04-01

    Application of a magnetoelastic rate of change of torque sensor to monitor the condition of milling cutters and operating parameters is described. Cutting tools naturally degrade with use by wear, chipping, or fracture, and the efficiency and quality of the product are highly dependent on the tool condition. The theoretical analysis is compared to experimental data in detecting changes in torque during each cutting event, and the rate of change of torque signal is investigated for a variety of cutting tool conditions.

  20. Chemometric methods applied to the calibration of a Vis-NIR sensor for gas engine's condition monitoring.

    PubMed

    Villar, Alberto; Gorritxategi, Eneko; Otaduy, Deitze; Ciria, Jose I; Fernandez, Luis A

    2011-10-31

    This paper describes the calibration process of a Visible-Near Infrared sensor for the condition monitoring of a gas engine's lubricating oil correlating transmittance oil spectra with the degradation of a gas engine's oil via a regression model. Chemometric techniques were applied to determine different parameters: Base Number (BN), Acid Number (AN), insolubles in pentane and viscosity at 40 °C. A Visible-Near Infrared (400-1100 nm) sensor developed in Tekniker research center was used to obtain the spectra of artificial and real gas engine oils. In order to improve sensor's data, different preprocessing methods such as smoothing by Saviztky-Golay, moving average with Multivariate Scatter Correction or Standard Normal Variate to eliminate the scatter effect were applied. A combination of these preprocessing methods was applied to each parameter. The regression models were developed by Partial Least Squares Regression (PLSR). In the end, it was shown that only some models were valid, fulfilling a set of quality requirements. The paper shows which models achieved the established validation requirements and which preprocessing methods perform better. A discussion follows regarding the potential improvement in the robustness of the models. PMID:21962360

  1. Development of a real time monitor and multivariate method for long term diagnostics of atmospheric pressure dielectric barrier discharges: application to He, He/N2, and He/O2 discharges.

    PubMed

    O'Connor, N; Milosavljević, V; Daniels, S

    2011-08-01

    In this paper we present the development and application of a real time atmospheric pressure discharge monitoring diagnostic. The software based diagnostic is designed to extract latent electrical and optical information associated with the operation of an atmospheric pressure dielectric barrier discharge (APDBD) over long time scales. Given that little is known about long term temporal effects in such discharges, the diagnostic methodology is applied to the monitoring of an APDBD in helium and helium with both 0.1% nitrogen and 0.1% oxygen gas admixtures over periods of tens of minutes. Given the large datasets associated with the experiments, it is shown that this process is much expedited through the novel application of multivariate correlations between the electrical and optical parameters of the corresponding chemistries which, in turn, facilitates comparisons between each individual chemistry also. The results of these studies show that the electrical and optical parameters of the discharge in helium and upon the addition of gas admixtures evolve over time scales far longer than the gas residence time and have been compared to current modelling works. It is envisaged that the diagnostic together with the application of multivariate correlations will be applied to rapid system identification and prototyping in both experimental and industrial APDBD systems in the future. PMID:21895242

  2. Condition Monitoring and Fault Diagnosis of Wet-Shift Clutch Transmission Based on Multi-technology

    NASA Astrophysics Data System (ADS)

    Chen, Man; Wang, Liyong; Ma, Biao

    Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and fault diagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis technology, function parameter test method and vibration analysis technology. The new fault diagnosis methods were proposed, which are to build the gray modeling with the oil analysis data, and to test the function parameter of the clutch press, the rotate speed of each gear, the oil press of the steer system and lubrication system and the hydraulic torque converter. It's validated that the representative function signals were chosen to execute the condition monitoring analysis, when the fault symptoms were found, and the oil analysis data were used to apply the gray modeling to forecast the fault occurs time can satisfy the demand of the condition monitoring and fault diagnosis for the transmission regular work.

  3. A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions

    NASA Astrophysics Data System (ADS)

    Antoniadou, I.; Manson, G.; Staszewski, W. J.; Barszcz, T.; Worden, K.

    2015-12-01

    This paper deals with the condition monitoring of wind turbine gearboxes under varying operating conditions. Generally, gearbox systems include nonlinearities so a simplified nonlinear gear model is developed, on which the time-frequency analysis method proposed is first applied for the easiest understanding of the challenges faced. The effect of varying loads is examined in the simulations and later on in real wind turbine gearbox experimental data. The Empirical Mode Decomposition (EMD) method is used to decompose the vibration signals into meaningful signal components associated with specific frequency bands of the signal. The mode mixing problem of the EMD is examined in the simulation part and the results in that part of the paper suggest that further research might be of interest in condition monitoring terms. For the amplitude-frequency demodulation of the signal components produced, the Hilbert Transform (HT) is used as a standard method. In addition, the Teager-Kaiser energy operator (TKEO), combined with an energy separation algorithm, is a recent alternative method, the performance of which is tested in the paper too. The results show that the TKEO approach is a promising alternative to the HT, since it can improve the estimation of the instantaneous spectral characteristics of the vibration data under certain conditions.

  4. Monitoring lining and hearth conditions at Inland`s No. 7 blast furnace

    SciTech Connect

    Quisenberry, P.; Grant, M.; Carter, W.

    1997-12-31

    The paper describes: furnace statistics; mini-reline undertaken in November, 1993; the stack condition; throat gunning; stabilizing the graphite bricks; the hearth condition; reactions to temperature excursions; future instrumentation; and hot blast system areas of concern. The present data from monitoring systems and inspections indicate that the furnace should be able to operate well beyond the expectation for the 1993 mini-reline (3--5 years) with: (1) consistent, high quality raw materials; (2) instrumentation, diagnostic, remedial, and preventative techniques developed; and (3) stopping quickly any water leaks into the furnace. The longevity of this campaign has undoubtedly been a result of this monitoring program.

  5. A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

    PubMed Central

    Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

    2014-01-01

    The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. PMID:24473281

  6. A wavelet bicoherence-based quadratic nonlinearity feature for translational axis condition monitoring.

    PubMed

    Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

    2014-01-01

    The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. PMID:24473281

  7. A New Application of Support Vector Machine Method: Condition Monitoring and Analysis of Reactor Coolant Pump

    NASA Astrophysics Data System (ADS)

    Meng, Qinghu; Meng, Qingfeng; Feng, Wuwei

    2012-05-01

    Fukushima nuclear power plant accident caused huge losses and pollution and it showed that the reactor coolant pump is very important in a nuclear power plant. Therefore, to keep the safety and reliability, the condition of the coolant pump needs to be online condition monitored and fault analyzed. In this paper, condition monitoring and analysis based on support vector machine (SVM) is proposed. This method is just to aim at the small sample studies such as reactor coolant pump. Both experiment data and field data are analyzed. In order to eliminate the noise and useless frequency, these data are disposed through a multi-band FIR filter. After that, a fault feature selection method based on principal component analysis is proposed. The related variable quantity is changed into unrelated variable quantity, and the dimension is descended. Then the SVM method is used to separate different fault characteristics. Firstly, this method is used as a two-kind classifier to separate each two different running conditions. Then the SVM is used as a multiple classifier to separate all of the different condition types. The SVM could separate these conditions successfully. After that, software based on SVM was designed for reactor coolant pump condition analysis. This software is installed on the reactor plant control system of Qinshan nuclear power plant in China. It could monitor the online data and find the pump mechanical fault automatically.

  8. Recommendations for strengthening the infrared technology component of any condition monitoring program

    NASA Astrophysics Data System (ADS)

    Nicholas, Jack R., Jr.; Young, R. K.

    1999-03-01

    This presentation provides insights of a long term 'champion' of many condition monitoring technologies and a Level III infra red thermographer. The co-authors present recommendations based on their observations of infra red and other components of predictive, condition monitoring programs in manufacturing, utility and government defense and energy activities. As predictive maintenance service providers, trainers, informal observers and formal auditors of such programs, the co-authors provide a unique perspective that can be useful to practitioners, managers and customers of advanced programs. Each has over 30 years experience in the field of machinery operation, maintenance, and support the origins of which can be traced to and through the demanding requirements of the U.S. Navy nuclear submarine forces. They have over 10 years each of experience with programs in many different countries on 3 continents. Recommendations are provided on the following: (1) Leadership and Management Support (For survival); (2) Life Cycle View (For establishment of a firm and stable foundation for a program); (3) Training and Orientation (For thermographers as well as operators, managers and others); (4) Analyst Flexibility (To innovate, explore and develop their understanding of machinery condition); (5) Reports and Program Justification (For program visibility and continued expansion); (6) Commitment to Continuous Improvement of Capability and Productivity (Through application of updated hardware and software); (7) Mutual Support by Analysts (By those inside and outside of the immediate organization); (8) Use of Multiple Technologies and System Experts to Help Define Problems (Through the use of correlation analysis of data from up to 15 technologies. An example correlation analysis table for AC and DC motors is provided.); (9) Root Cause Analysis (Allows a shift from reactive to proactive stance for a program); (10) Master Equipment Identification and Technology Application (To

  9. Monitoring of WUT grand hall roof in conditions of high temperature changes

    NASA Astrophysics Data System (ADS)

    Wozniak, M.

    2009-04-01

    The geodetic control measurements of changes in object's geometry should satisfy high accuracy and reliability. New tacheometers equipped with Automatic Target Recognition automatically moves the telescope to the center of the prism and supports control points measurements. The accuracy of using ATR system and stability of instrument in precise measurements were controlled in laboratory and field conditions. This paper will present the results of monitoring measurements using Leica TDA 5005 during investigations of roof geometry in conditions of high temperature changes.

  10. Railway track component condition monitoring using optical fibre Bragg grating sensors

    NASA Astrophysics Data System (ADS)

    Buggy, S. J.; James, S. W.; Staines, S.; Carroll, R.; Kitson, P.; Farrington, D.; Drewett, L.; Jaiswal, J.; Tatam, R. P.

    2016-05-01

    The use of optical fibre Bragg grating (FBG) strain sensors to monitor the condition of safety critical rail components is investigated. Fishplates, switchblades and stretcher bars on the Stagecoach Supertram tramway in Sheffield in the UK have been instrumented with arrays of FBG sensors. The dynamic strain signatures induced by the passage of a tram over the instrumented components have been analysed to identify features indicative of changes in the condition of the components.

  11. A Recursive Multiscale Correlation-Averaging Algorithm for an Automated Distributed Road Condition Monitoring System

    SciTech Connect

    Ndoye, Mandoye; Barker, Alan M; Krogmeier, James; Bullock, Darcy

    2011-01-01

    A signal processing approach is proposed to jointly filter and fuse spatially indexed measurements captured from many vehicles. It is assumed that these measurements are influenced by both sensor noise and measurement indexing uncertainties. Measurements from low-cost vehicle-mounted sensors (e.g., accelerometers and Global Positioning System (GPS) receivers) are properly combined to produce higher quality road roughness data for cost-effective road surface condition monitoring. The proposed algorithms are recursively implemented and thus require only moderate computational power and memory space. These algorithms are important for future road management systems, which will use on-road vehicles as a distributed network of sensing probes gathering spatially indexed measurements for condition monitoring, in addition to other applications, such as environmental sensing and/or traffic monitoring. Our method and the related signal processing algorithms have been successfully tested using field data.

  12. Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review

    NASA Astrophysics Data System (ADS)

    Mehrjou, Mohammad Rezazadeh; Mariun, Norman; Hamiruce Marhaban, Mohammad; Misron, Norhisam

    2011-11-01

    Nowadays, manufacturing companies are making great efforts to implement an effective machinery maintenance program, which provides incipient fault detection. The machine problem and its irregularity can be detected at an early stage by employing a suitable condition monitoring accompanied with powerful signal processing technique. Among various defects occurred in machines, rotor faults are of significant importance as they cause secondary failures that lead to a serious motor malfunction. Diagnosis of rotor failures has long been an important but complicated task in the area of motor faults detection. This paper intends to review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection. The aim of this article is to provide a broad outlook on rotor fault monitoring techniques for the researchers and engineers.

  13. Monitoring diapause development in the Colorado potato beetle, Leptinotarsa decemlineata, under field conditions using molecular biomarkers

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A multiplex PCR protocol was developed using five diapause-regulated genes to monitor diapause development of the Colorado potato beetle under field conditions. A total of 870 beetles from the Red River Valley of North Dakota and Minnesota, USA, were screened for three consecutive years. Out of the ...

  14. Online Condition Monitoring to Enable Extended Operation of Nuclear Power Plants

    SciTech Connect

    Meyer, Ryan M.; Bond, Leonard J.; Ramuhalli, Pradeep

    2012-03-31

    Safe, secure, and economic operation of nuclear power plants will remain of strategic significance. New and improved monitoring will likely have increased significance in the post-Fukushima world. Prior to Fukushima, many activities were already underway globally to facilitate operation of nuclear power plants beyond their initial licensing periods. Decisions to shut down a nuclear power plant are mostly driven by economic considerations. Online condition monitoring is a means to improve both the safety and economics of extending the operating lifetimes of nuclear power plants, enabling adoption of proactive aging management. With regard to active components (e.g., pumps, valves, motors, etc.), significant experience in other industries has been leveraged to build the science base to support adoption for online condition-based maintenance and proactive aging management in the nuclear industry. Many of the research needs are associated with enabling proactive management of aging in passive components (e.g., pipes, vessels, cables, containment structures, etc.). This paper provides an overview of online condition monitoring for the nuclear power industry with an emphasis on passive components. Following the overview, several technology/knowledge gaps are identified, which require addressing to facilitate widespread online condition monitoring of passive components.

  15. Implementation of an Integrated, Portable Transformer Condition Monitoring Instrument in the Classroom and On-Site

    ERIC Educational Resources Information Center

    Chatterjee, B.; Dey, D.; Chakravorti, S.

    2010-01-01

    The development of integrated, portable, transformer condition monitoring (TCM) equipment for classroom demonstrations as well as for student exercises conducted in the field is discussed. Demonstrations include experimentation with real-world transformers to illustrate concepts such as polarization and depolarization current through oil-paper…

  16. Wind Turbine Drivetrain Condition Monitoring During GRC Phase 1 and Phase 2 Testing

    SciTech Connect

    Sheng, S.; Link, H.; LaCava, W.; van Dam, J.; McNiff, B.; Veers, P.; Keller, J.; Butterfield, S.; Oyague, F.

    2011-10-01

    This report will present the wind turbine drivetrain condition monitoring (CM) research conducted under the phase 1 and phase 2 Gearbox Reliability Collaborative (GRC) tests. The rationale and approach for this drivetrain CM research, investigated CM systems, test configuration and results, and a discussion on challenges in wind turbine drivetrain CM and future research and development areas, will be presented.

  17. 7 CFR 623.16 - Monitoring and enforcement of easement terms and conditions.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 7 Agriculture 6 2010-01-01 2010-01-01 false Monitoring and enforcement of easement terms and conditions. 623.16 Section 623.16 Agriculture Regulations of the Department of Agriculture (Continued) NATURAL RESOURCES CONSERVATION SERVICE, DEPARTMENT OF AGRICULTURE WATER RESOURCES EMERGENCY...

  18. Integrating remote sensing data from multiple optical sensors for ecological and crop condition monitoring

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Ecological and crop condition monitoring requires high temporal and spatial resolution remote sensing data. Due to technical limitations and budget constraints, remote sensing instruments trade spatial resolution for swath width. As a result, it is difficult to acquire remotely sensed data with both...

  19. The Role of Sample Surveys for Monitoring the Condition of the Nation's Lakes.

    ERIC Educational Resources Information Center

    Larsen, D. P.; And Others

    1994-01-01

    This paper describes the need for statistically based survey design. It briefly summarizes the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) and demonstrates how its design is tailored for the selection of a probability sample of lakes on which to make measurements of lake conditions. (LZ)

  20. 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.

  1. Fluorescent and Luminescent Probes for Monitoring Hydroxyl Radical under Biological Conditions.

    PubMed

    Żamojć, Krzysztof; Zdrowowicz, Magdalena; Jacewicz, Dagmara; Wyrzykowski, Dariusz; Chmurzyński, Lech

    2016-01-01

    Detection and quantitative determination in biological media of the hydroxyl radical are of great importance due to the role this radical plays in many physiological and pathological processes. This review focuses on the progress that has been made in recent years in the development of fluorescent and luminescent probes employed to monitor hydroxyl radical concentrations under biological conditions. PMID:26042844

  2. Assessing the ecological condition of streams in a southeastern Brazilian basin using a probabilistic monitoring design.

    PubMed

    Jiménez-Valencia, Juliana; Kaufmann, Philip R; Sattamini, Ana; Mugnai, Riccardo; Baptista, Darcilio Fernandes

    2014-08-01

    Prompt assessment and management actions are required if we are to reduce the current rapid loss of habitat and biodiversity worldwide. Statistically valid quantification of the biota and habitat condition in water bodies are prerequisites for rigorous assessment of aquatic biodiversity and habitat. We assessed the ecological condition of streams in a southeastern Brazilian basin. We quantified the percentage of stream length in good, fair, and poor ecological condition according to benthic macroinvertebrate assemblage. We assessed the risk of finding degraded ecological condition associated with degraded aquatic riparian physical habitat condition, watershed condition, and water quality. We describe field sampling and implementation issues encountered in our survey and discuss design options to remedy them. Survey sample sites were selected using a spatially balanced, stratified random design, which enabled us to put confidence bounds on the ecological condition estimates derived from the stream survey. The benthic condition index indicated that 62 % of stream length in the basin was in poor ecological condition, and 13 % of stream length was in fair condition. The risk of finding degraded biological condition when the riparian vegetation and forests in upstream catchments were degraded was 2.5 and 4 times higher, compared to streams rated as good for the same stressors. We demonstrated that the GRTS statistical sampling method can be used routinely in Brazilian rain forests and other South American regions with similar conditions. This survey establishes an initial baseline for monitoring the condition and trends of streams in the region. PMID:24829159

  3. Multivariate optimization and supplementation strategies for the simultaneous production of amylases, cellulases, xylanases, and proteases by Aspergillus awamori under solid-state fermentation conditions.

    PubMed

    de Castro, Aline Machado; Castilho, Leda R; Freire, Denise Maria Guimarães

    2015-02-01

    The production of extracts containing a pool of enzymes for extensive biomass deconstruction can lead to significant advantages in biorefinery applications. In this work, a strain of Aspergillus awamori IOC-3914 was used for the simultaneous production of five groups of hydrolases by solid-state fermentation of babassu cake. Sequential experimental design strategies and multivariate optimization using the desirability function were first used to study temperature, moisture content, and granulometry. After that, further improvements in product yields were achieved by supplementation with other agro-industrial materials. At the end of the study, the production of enzymes was up to 3.3-fold increased, and brewer's spent grains and babassu flour showed to be the best supplements. Maximum activities for endoamylases, exoamylases, cellulases (CMCases), xylanases, and proteases achieved were 197, 106, 20, 835, and 57 U g(-1), respectively. The strain was also able to produce β-glucosidases and debranching amylases (up to 35 and 43 U g(-1), respectively), indicating the potential of its enzyme pool for cellulose and starch degradation. PMID:25413792

  4. Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

    SciTech Connect

    Kumar, Rajeeva; Kumar, Aditya; Dai, Dan; Seenumani, Gayathri; Down, John; Lopez, Rodrigo

    2012-12-31

    This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve

  5. The Piston Compressor: The Methodology of the Real-Time Condition Monitoring

    NASA Astrophysics Data System (ADS)

    Naumenko, A. P.; Kostyukov, V. N.

    2012-05-01

    The methodology of a diagnostic signal processing, a function chart of the monitoring system are considered in the article. The methodology of monitoring and diagnosing is based on measurement of indirect processes' parameters (vibroacoustic oscillations) therefore no more than five sensors is established on the cylinder, measurement of direct structural and thermodynamic parameters is envisioned as well. The structure and principle of expert system's functioning of decision-making is given. Algorithm of automatic expert system includes the calculation diagnostic attributes values based on their normative values, formation sets of diagnostic attributes that correspond to individual classes to malfunction, formation of expert system messages. The scheme of a real-time condition monitoring system for piston compressors is considered. The system have consistently-parallel structure of information-measuring equipment, which allows to measure the vibroacoustic signal for condition monitoring of reciprocating compressors and modes of its work. Besides, the system allows to measure parameters of other physical processes, for example, system can measure and use for monitoring and statements of the diagnosis the pressure in decreasing spaces (the indicator diagram), the inlet pressure and flowing pressure of each cylinder, inlet and delivery temperature of gas, valves temperature, position of a rod, leakage through compression packing and others.

  6. Monitoring Surface Condition of Plasma Grid of a Negative Hydrogen Ion Source

    SciTech Connect

    Wada, M.; Kasuya, T.; Tokushige, S.; Kenmotsu, T.

    2011-09-26

    Surface condition of a plasma grid in a negative hydrogen ion source is controlled so as to maximize the beam current under a discharge operation with introducing Cs into the ion source. Photoelectric current induced by laser beams incident on the plasma grid can produce a signal to monitor the surface condition, but the signal detection can be easily hindered by plasma noise. Reduction in size of a detection electrode embedded in the plasma grid can improve signal-to-noise ratio of the photoelectric current from the electrode. To evaluate the feasibility of monitoring surface condition of a plasma gird by utilizing photoelectric effect, a small experimental setup capable of determining quantum yields of a surface in a cesiated plasma environment is being assembled. Some preliminary test results of the apparatus utilizing oxide cathodes are reported.

  7. Unified Multi-speed analysis (UMA) for the condition monitoring of aero-engines

    NASA Astrophysics Data System (ADS)

    Nembhard, Adrian D.; Sinha, Jyoti K.

    2015-12-01

    For rotating machinery in which speeds and dynamics constantly change, performing vibration-based condition monitoring can be challenging. Thus, an effort is made here to develop a Unified Multi-speed fault diagnosis technique that can exploit useful vibration information available at various speeds from a rotating machine in a single analysis. Commonly applied indicators are computed from data collected from a rig at different speeds for a baseline case and different faults. Four separate analyses are performed: single speed at a single bearing, integrated features from multiple speeds at a single bearing, single speed for integrated features from multiple bearings and the proposed Unified Multi-speed analysis. The Unified Multi-speed approach produces the most conspicuous separation and isolation among the conditions tested. Observations made here suggest integration of more dynamic features available at different speeds improves the learning process of the tool which could prove useful for aero-engine condition monitoring.

  8. Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods

    SciTech Connect

    Liao, T. W.; Hua, G; Qu, Jun; Blau, Peter Julian

    2006-01-01

    Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.

  9. Multivariable PID control by decoupling

    NASA Astrophysics Data System (ADS)

    Garrido, Juan; Vázquez, Francisco; Morilla, Fernando

    2016-04-01

    This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.

  10. Research of on-line monitoring method for insulation condition of power transformer bushing

    NASA Astrophysics Data System (ADS)

    Xia, Jiuyun; Qian, Zheng; Yu, Hao; Yao, Junda

    2016-01-01

    The power transformer is the key equipment of the power system; its insulation condition will directly influence the security and reliability of the power system. Thus, the on-line monitoring of power transformer is urgently required in order to guarantee the normal operation of the power system. Moreover, the dielectric loss factor is a significant parameter reflecting the condition of transformer bushing, so the on-line measurement of dielectric loss factor is really important. In this paper, the phase-to-phase comparison method is selected as the on-line monitoring method based on the overall analysis and discussion of the existing on-line monitoring methods. At first, the harmonic analysis method is utilized to calculate the dielectric loss of each phase of the three-phase transformer bushing, and then the differences of dielectric loss between every two phases are calculated and analyzed. So the insulation condition of each bushing could be achieved based on the careful analysis of different phase-to-phase dielectric loss. The simulation results of phase-to-phase comparison method are carried out in this paper, and the validity is verified. At last, this method is utilized in an actual equipment of on-line monitoring.

  11. Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

    ERIC Educational Resources Information Center

    Thompson, Bruce

    Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

  12. An Updated Methodology for Enhancing Risk Monitors with Integrated Equipment Condition Assessment

    SciTech Connect

    Ramuhalli, Pradeep; Hirt, Evelyn H.; Coles, Garill A.; Bonebrake, Christopher A.; Ivans, William J.; Wootan, David W.; Mitchell, Mark R.

    2014-07-18

    Small modular reactors (SMRs) generally include reactors with electric output of ~350 MWe or less (this cutoff varies somewhat but is substantially less than full-size plant output of 700 MWe or more). Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. Enhancing affordability of AdvSMRs will be critical to ensuring wider deployment, as AdvSMRs suffer from loss of economies of scale inherent in small reactors when compared to large (~greater than 600 MWe output) reactors and the controllable day-to-day costs of AdvSMRs will be dominated by operation and maintenance (O&M) costs. Technologies that help characterize real-time risk are important for controlling O&M costs. Risk monitors are used in current nuclear power plants to provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, and environmental conditions). However, current risk monitors are unable to support the capability requirements listed above as they do not take into account plant-specific normal, abnormal, and deteriorating states of active components and systems. This report documents technology developments towards enhancing risk monitors that, if integrated with supervisory plant control systems, can provide the capability requirements listed and meet the goals of controlling O&M costs. The report describes research results on augmenting an initial methodology for enhanced risk monitors that integrate real-time information about equipment condition and POF into risk monitors. Methods to propagate uncertainty through the enhanced risk monitor are evaluated. Available data to quantify the level of uncertainty and the POF of key components are examined for their relevance, and a status update of this data evaluation is described. Finally, we describe potential targets for developing new risk metrics that may be useful for studying trade-offs for economic

  13. A multivariate CAR model for mismatched lattices.

    PubMed

    Porter, Aaron T; Oleson, Jacob J

    2014-10-01

    In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey. PMID:25457598

  14. Perspectives on railway track geometry condition monitoring from in-service railway vehicles

    NASA Astrophysics Data System (ADS)

    Weston, P.; Roberts, C.; Yeo, G.; Stewart, E.

    2015-07-01

    This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feeding their geometry measurements into large databases. These data can be retrieved, but little is currently done with the data other than the generation of reports of track geometry that exceeds predefined thresholds. There are examples of simpler systems that measure some track geometry parameters more or less directly and accurately, but forego parameters such as gauge. Additionally, there are experimental systems that use mathematics and models to infer track geometry using data from sensors placed on an in-service vehicle. Finally, there are systems that do not claim to measure track geometry, but monitor some other quantity such as ride quality or bogie acceleration to infer poor track geometry without explicitly measuring it.

  15. Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops.

    PubMed

    Zhang, Cunji; Yao, Xifan; Zhang, Jianming

    2015-01-01

    Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi(®) Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops. PMID:26633418

  16. Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops

    PubMed Central

    Zhang, Cunji; Yao, Xifan; Zhang, Jianming

    2015-01-01

    Radio Frequency Identification (RFID) technology has been widely used in many fields. However, previous studies have mainly focused on product life cycle tracking, and there are few studies on real-time status monitoring of workpieces in manufacturing workshops. In this paper, a wisdom manufacturing model is introduced, a sensing-aware environment for a wisdom manufacturing workshop is constructed, and RFID event models are defined. A synthetic data cleaning method is applied to clean the raw RFID data. The Complex Event Processing (CEP) technology is adopted to monitor abnormal conditions of workpieces in real time. The RFID data cleaning method and data mining technology are examined by simulation and physical experiments. The results show that the synthetic data cleaning method preprocesses data well. The CEP based on the Rifidi® Edge Server technology completed abnormal condition monitoring of workpieces in real time. This paper reveals the importance of RFID spatial and temporal data analysis in real-time status monitoring of workpieces in wisdom manufacturing workshops. PMID:26633418

  17. An Uncertainty Quantification Framework for Prognostics and Condition-Based Monitoring

    NASA Technical Reports Server (NTRS)

    Sankararaman, Shankar; Goebel, Kai

    2014-01-01

    This paper presents a computational framework for uncertainty quantification in prognostics in the context of condition-based monitoring of aerospace systems. The different sources of uncertainty and the various uncertainty quantification activities in condition-based prognostics are outlined in detail, and it is demonstrated that the Bayesian subjective approach is suitable for interpreting uncertainty in online monitoring. A state-space model-based framework for prognostics, that can rigorously account for the various sources of uncertainty, is presented. Prognostics consists of two important steps. First, the state of the system is estimated using Bayesian tracking, and then, the future states of the system are predicted until failure, thereby computing the remaining useful life of the system. The proposed framework is illustrated using the power system of a planetary rover test-bed, which is being developed and studied at NASA Ames Research Center.

  18. Fusing human knowledge with neural networks in machine condition monitoring systems

    NASA Astrophysics Data System (ADS)

    Melvin, David G.; Penman, J.

    1995-04-01

    There is currently much interest in the application of artificial neural network (ANN) technology to the field of on-line machine condition monitoring (CM) for complex electro- mechanical systems. In this paper the authors discuss, with the help of an industrial case study, a few of the difficulties inherent in the application of neural network based condition monitoring. A method of overcoming these difficulties by utilizing a combination of human knowledge, encoded using techniques borrowed from fuzzy logic, Kohonen neural networks, and statistical K-means clustering has been constructed. The methodology is discussed in the paper by means of a direct comparison between this new approach and a purely neural approach. An analysis of other situations where this approach would be applicable is also presented and the paper discusses other current research work in the area of hybrid AI technologies which should assist further with the alleviation of the problems under consideration.

  19. DEVELOPMENT OF A SENSOR NETWORK TEST BED FOR ISD MATERIALS AND STRUCUTRAL CONDITION MONITORING

    SciTech Connect

    Zeigler, K.; Ferguson, B.; Karapatakis, D.; Herbst, C.; Stripling, C.

    2011-07-06

    The P Reactor at the Savannah River Site is one of the first reactor facilities in the US DOE complex that has been placed in its end state through in situ decommissioning (ISD). The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. To evaluate the feasibility and utility of remote sensors to provide verification of ISD system conditions and performance characteristics, an ISD Sensor Network Test Bed has been designed and deployed at the Savannah River National Laboratory. The test bed addresses the DOE-EM Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of: (1) Groutable thermistors for temperature and moisture monitoring; (2) Strain gauges for crack growth monitoring; (3) Tiltmeters for settlement monitoring; and (4) A communication system for data collection. Preliminary baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment.

  20. [Fundamentals of socio-hygienic monitoring of environmental conditions for students of higher education schools].

    PubMed

    Blinova, E G; Kuchma, V R

    2012-01-01

    Socioeconomic transformations and the poor environment of an industrial megalopolis negatively affected quality of life and morbidity rates in students (n = 2160). Academic intensity contributed to an increase in overall morbidity and morbidity from nervous system involvement. The regional sociohygienic monitoring of high-school training conditions within the framework of the surveillance system substantiates programs to prevent worse health and life quality in high school students. PMID:22712322

  1. Development of a wireless bridge monitoring system for condition assessment using hybrid techniques

    NASA Astrophysics Data System (ADS)

    Whelan, Matthew J.; Fuchs, Michael P.; Gangone, Michael V.; Janoyan, Kerop D.

    2007-04-01

    The introduction and development of wireless sensor network technology has resulted in rapid growth within the field of structural health monitoring (SHM), as the dramatic cable costs associated with instrumentation of large civil structures is potentially alleviated. Traditionally, condition assessment of bridge structures is accomplished through the use of either vibration measurements or strain sensing. One approach is through quantifying dynamic characteristics and mode shapes developed through the use of relatively dense arrays of accelerometers. Another widely utilized method of condition assessment is bridge load rating, which is enabled through the use of strain sensors. The Wireless Sensor Solution (WSS) developed specifically for diagnostic bridge monitoring provides a hybrid system that interfaces with both accelerometers and strain sensors to facilitate vibration-based bridge evaluation as well as load rating and static analysis on a universal platform. This paper presents the development and testing of a wireless bridge monitoring system designed within the Laboratory for Intelligent Infrastructure and Transportation Technologies (LIITT) at Clarkson University. The system interfaces with low-cost MEMS accelerometers using custom signal conditioning for amplification and filtering tailored to the spectrum of typical bridge vibrations, specifically from ambient excitation. Additionally, a signal conditioning and high resolution ADC interface is provided for strain gauge sensors. To permit compensation for the influence of temperature, thermistor-based temperature sensing is also enabled. In addition to the hardware description, this paper presents features of the software applications and host interface developed for flexible, user-friendly in-network control of and acquisition from the sensor nodes. The architecture of the software radio protocol is also discussed along with results of field deployments including relatively large-scale networks and

  2. Monitoring of Double-Stud Wall Moisture Conditions in the Northeast

    SciTech Connect

    Ueno, K.

    2015-03-01

    Double-stud walls insulated with cellulose or low-density spray foam can have R-values of 40 or higher. However, double-stud walls have a higher risk of interior-sourced condensation moisture damage when compared with high-R approaches using exterior insulating sheathing. Moisture conditions in double-stud walls were monitored in Zone 5A (Massachusetts); three double-stud assemblies were compared.

  3. Development of an In-Situ Decommissioning Sensor Network Test Bed for Structural Condition Monitoring - 12156

    SciTech Connect

    Zeigler, Kristine E.; Ferguson, Blythe A.

    2012-07-01

    The Savannah River National Laboratory (SRNL) has established an In Situ Decommissioning (ISD) Sensor Network Test Bed, a unique, small scale, configurable environment, for the assessment of prospective sensors on actual ISD system material, at minimal cost. The Department of Energy (DOE) is presently implementing permanent entombment of contaminated, large nuclear structures via ISD. The ISD end state consists of a grout-filled concrete civil structure within the concrete frame of the original building. Validation of ISD system performance models and verification of actual system conditions can be achieved through the development a system of sensors to monitor the materials and condition of the structure. The ISD Sensor Network Test Bed has been designed and deployed to addresses the DOE-Environmental Management Technology Need to develop a remote monitoring system to determine and verify ISD system performance. Commercial off-the-shelf sensors have been installed on concrete blocks taken from walls of the P Reactor Building at the Savannah River Site. Deployment of this low-cost structural monitoring system provides hands-on experience with sensor networks. The initial sensor system consists of groutable thermistors for temperature and moisture monitoring, strain gauges for crack growth monitoring, tilt-meters for settlement monitoring, and a communication system for data collection. Baseline data and lessons learned from system design and installation and initial field testing will be utilized for future ISD sensor network development and deployment. The Sensor Network Test Bed at SRNL uses COTS sensors on concrete blocks from the outer wall of the P Reactor Building to measure conditions expected to occur in ISD structures. Knowledge and lessons learned gained from installation, testing, and monitoring of the equipment will be applied to sensor installation in a meso-scale test bed at FIU and in future ISD structures. The initial data collected from the sensors

  4. 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

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

    PubMed

    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

  6. 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.

  7. Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography

    NASA Astrophysics Data System (ADS)

    Huda, A. S. N.; Taib, S.

    2013-11-01

    Monitoring the thermal condition of electrical equipment is necessary for maintaining the reliability of electrical system. The degradation of electrical equipment can cause excessive overheating, which can lead to the eventual failure of the equipment. Additionally, failure of equipment requires a lot of maintenance cost, manpower and can also be catastrophic- causing injuries or even deaths. Therefore, the recognition processof equipment conditions as normal and defective is an essential step towards maintaining reliability and stability of the system. The study introduces infrared thermography based condition monitoring of electrical equipment. Manual analysis of thermal image for detecting defects and classifying the status of equipment take a lot of time, efforts and can also lead to incorrect diagnosis results. An intelligent system that can separate the equipment automatically could help to overcome these problems. This paper discusses an intelligent classification system for the conditions of equipment using neural networks. Three sets of features namely first order histogram based statistical, grey level co-occurrence matrix and component based intensity features are extracted by image analysis, which are used as input data for the neural networks. The multilayered perceptron networks are trained using four different training algorithms namely Resilient back propagation, Bayesian Regulazation, Levenberg-Marquardt and Scale conjugate gradient. The experimental results show that the component based intensity features perform better compared to other two sets of features. Finally, after selecting the best features, multilayered perceptron network trained using Levenberg-Marquardt algorithm achieved the best results to classify the conditions of electrical equipment.

  8. Usefulness of LANDSAT data for monitoring plant development and range conditions in California's annual grassland

    NASA Technical Reports Server (NTRS)

    Carneggie, D. M.; Degloria, S. D.; Colwell, R. N.

    1975-01-01

    A network of sampling sites throughout the annual grassland region of California was established to correlate plant growth stages and forage production to climatic and other environmental factors. Plant growth and range conditions were further related to geographic location and seasonal variations. A sequence of LANDSAT data was obtained covering critical periods in the growth cycle. This was analyzed by both photointerpretation and computer aided techniques. Image characteristics and spectral reflectance data were then related to forage production, range condition, range site and changing growth conditions. It was determined that repeat sequences with LANDSAT color composite images do provide a means for monitoring changes in range condition. Spectral radiance data obtained from magnetic tape can be used to determine quantitatively the critical stages in the forage growth cycle. A computer ratioing technique provided a sensitive indicator of changes in growth stages and an indication of the relative differences in forage production between range sites.

  9. Nonlinearity detection for condition monitoring utilizing higher-order spectral analysis diagnostics

    NASA Astrophysics Data System (ADS)

    Park, Hyeonsu

    In this dissertation, we investigate the theory and application of higher-order spectral analysis techniques to condition monitoring in shipboard electrical power systems. Monitoring and early detection of faults in rotating machines, such as induction motors, are essential for both preventive maintenance and to avoid potentially severe damage. As machines degrade, they often tend to become more nonlinear. This increased nonlinearity results in the introduction of new frequencies which satisfy particular frequency selection rules; the exact selection rule depends on the order of the nonlinearity. In addition, the phases of the newly generated frequencies satisfy a similar phase selection rule. This results in a phase coherence, or phase coupling, between the "original" interacting frequencies and the "new" frequencies. This phase coupling is a true signature of nonlinearity. Since the classical auto-power spectrum contains no phase information, the phase coupling signature associated with nonlinear interactions is not available. However, various higher-order spectra (HOS) are capable of detecting such nonlinear-induced phase coupling. The efficacy of the various proposed HOS-based methodologies is investigated using real-world vibration time-series data from a faulted induction motor driving a dc generator. The fault is controlled by varying a resistor placed in one phase of the three-phase line to the induction motor. First, we propose a novel method using a bispectral change detection (BCD) for condition monitoring. Even though the bicoherence is dominant and powerful in the detection of phase coupling of nonlinearly interacting frequencies, it has some difficulties in its application to machine condition monitoring. Basically, the bicoherence may not be able to distinguish between intrinsic nonlinearities associated with healthy machines and fault-induced nonlinearities. Therefore, the ability to discriminate the fault-only nonlinearities from the intrinsic

  10. Technical Report on Preliminary Methodology for Enhancing Risk Monitors with Integrated Equipment Condition Assessment

    SciTech Connect

    Ramuhalli, Pradeep; Coles, Garill A.; Coble, Jamie B.; Hirt, Evelyn H.

    2013-09-17

    Small modular reactors (SMRs) generally include reactors with electric output of ~350 MWe or less (this cutoff varies somewhat but is substantially less than full-size plant output of 700 MWe or more). Advanced SMRs (AdvSMRs) refer to a specific class of SMRs and are based on modularization of advanced reactor concepts. AdvSMRs may provide a longer-term alternative to traditional light-water reactors (LWRs) and SMRs based on integral pressurized water reactor concepts currently being considered. Enhancing affordability of AdvSMRs will be critical to ensuring wider deployment. AdvSMRs suffer from loss of economies of scale inherent in small reactors when compared to large (~greater than 600 MWe output) reactors. Some of this loss can be recovered through reduced capital costs through smaller size, fewer components, modular fabrication processes, and the opportunity for modular construction. However, the controllable day-to-day costs of AdvSMRs will be dominated by operation and maintenance (O&M) costs. Technologies that help characterize real-time risk are important for controlling O&M costs. Risk monitors are used in current nuclear power plants to provide a point-in-time estimate of the system risk given the current plant configuration (e.g., equipment availability, operational regime, and environmental conditions). However, current risk monitors are unable to support the capability requirements listed above as they do not take into account plant-specific normal, abnormal, and deteriorating states of active components and systems. This report documents technology developments that are a step towards enhancing risk monitors that, if integrated with supervisory plant control systems, can provide the capability requirements listed and meet the goals of controlling O&M costs. The report describes research results from an initial methodology for enhanced risk monitors by integrating real-time information about equipment condition and POF into risk monitors.

  11. 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.

  12. Field monitoring of condition of large electric generators. (Latest citations from the EI Compendex plus database). Published Search

    SciTech Connect

    Not Available

    1993-08-01

    The bibliography contains citations concerning monitoring techniques to determine the condition of large electric generators. Electric generators are limited to turbine generators, variously called hydroturbines, turbogenerators and turbosets. Wind turbines and magnetohydrodynamics are not included in this bibliography. Techniques for condition monitoring include noise analysis and acoustic monitoring, vibration and misalignment measurements, bearing oil analyses, and transient torsional changes affecting shafts and rotors. (Contains a minimum of 178 citations and includes a subject term index and title list.)

  13. Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions

    PubMed Central

    Thap, Tharoeun; Yoon, Kwon-Ha; Lee, Jinseok

    2016-01-01

    We proposed new electrodes that are applicable for electrocardiogram (ECG) monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL), a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS) electrode and a pencil lead powder type (PLP) electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes. PMID:27092502

  14. Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions.

    PubMed

    Thap, Tharoeun; Yoon, Kwon-Ha; Lee, Jinseok

    2016-01-01

    We proposed new electrodes that are applicable for electrocardiogram (ECG) monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL), a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS) electrode and a pencil lead powder type (PLP) electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes. PMID:27092502

  15. Condition health monitoring of composite wound pressure vessels using fiber Bragg gratings

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaojing; Zhang, Boming; Wu, Zhanjun

    2007-07-01

    Structure health monitoring refers to a real time and in situ monitoring system. It can diagnose the condition status of composite structure in time and effectively estimate the safety, increasing the reliability, extending the service life, at the same time, reducing the maintenance cost. In this paper, the sensing technology based on FBG sensors is employed to monitor the health of composite wound pressure vessel in service. Strain monitoring of the vessel in fatigue tests is carried out with the surface mounted FBG sensors. The experiment result shows that FBG sensors have several excellent performances: it has anti-fatigue capability to accurately measure the cycle strain; it is linear with the inner pressure and can be used as pressure sensor; the wavelength is diverged in the high stress gradient field, so the FBG can be used to measure the non-homogeneous strain field. Based on the fatigue damage mechanism of composite laminates and stiffness degradation model, the variation regularity of cycle strain of composite pressure vessel is studied and the residual stiffness during damage is obtained.

  16. Optimizing groundwater monitoring systems for landfills with random leaks under heterogeneous subsurface conditions

    NASA Astrophysics Data System (ADS)

    Yenigül, N. B.; Elfeki, A. M. M.; van den Akker, C.; Dekking, F. M.

    2013-12-01

    Landfills are one of the most common human activities threatening the natural groundwater quality. The landfill may leak, and the corresponding plumes may contaminate an area, entailing costly remediation measures. The objective of the installation of monitoring systems at landfill sites is to detect the contaminant plumes before they reach the regulatory compliance boundary in order to enable cost-effective counter measures. In this study, a classical decision analysis approach is linked to a stochastic simulation model to determine the optimal groundwater monitoring system given uncertainties due to the hydrogeological conditions and contaminant source characteristics. A Monte Carlo approach is used to incorporate uncertainties. Hydraulic conductivity and the leak location are the random inputs of the simulation model. The design objectives are to: (1) maximize the detection probability, (2) minimize the area of contamination at the time of detection, and (3) minimize the total cost of the monitoring system. A synthetic test case based on a real-world case in the Netherlands is analyzed. The results show that monitoring systems located close to the source are optimal except for the cases with very high unit installation and sampling cost and/or very cheap unit remediation.

  17. Wireless monitoring of the longitudinal displacement of the Tamar Suspension Bridge deck under changing environmental conditions

    NASA Astrophysics Data System (ADS)

    de Battista, Nicky; Westgate, Robert; Koo, Ki Young; Brownjohn, James

    2011-04-01

    In order to be able to monitor the performance and health of a civil structure it is essential to understand how it behaves under different environmental conditions. It is a well documented fact that the structural performance of bridges can be altered considerably when they are subjected to changes in environmental conditions. This paper presents a study investigating the longitudinal movement of the road deck on Tamar Suspension Bridge in Plymouth in the UK over six months. The expansion joint of the bridge deck was instrumented with pull-wire type extensometers. The data were transmitted wirelessly using commercial wireless sensor nodes and collected at a data acquisition laptop computer, which was accessible online for remote monitoring. In addition, position data of various locations on the bridge deck were collected using a Robotic Total Station (RTS). Environmental data, such as the temperature, and structural data, such as cable tension, were acquired from other monitoring systems. Conclusions drawn from a fusion of the bridge deck's longitudinal displacement with other structural and environmental data are discussed in this paper.

  18. Condition monitoring through advanced sensor and computational technology : final report (January 2002 to May 2005).

    SciTech Connect

    Kim, Jung-Taek; Luk, Vincent K.

    2005-05-01

    The overall goal of this joint research project was to develop and demonstrate advanced sensors and computational technology for continuous monitoring of the condition of components, structures, and systems in advanced and next-generation nuclear power plants (NPPs). This project included investigating and adapting several advanced sensor technologies from Korean and US national laboratory research communities, some of which were developed and applied in non-nuclear industries. The project team investigated and developed sophisticated signal processing, noise reduction, and pattern recognition techniques and algorithms. The researchers installed sensors and conducted condition monitoring tests on two test loops, a check valve (an active component) and a piping elbow (a passive component), to demonstrate the feasibility of using advanced sensors and computational technology to achieve the project goal. Acoustic emission (AE) devices, optical fiber sensors, accelerometers, and ultrasonic transducers (UTs) were used to detect mechanical vibratory response of check valve and piping elbow in normal and degraded configurations. Chemical sensors were also installed to monitor the water chemistry in the piping elbow test loop. Analysis results of processed sensor data indicate that it is feasible to differentiate between the normal and degraded (with selected degradation mechanisms) configurations of these two components from the acquired sensor signals, but it is questionable that these methods can reliably identify the level and type of degradation. Additional research and development efforts are needed to refine the differentiation techniques and to reduce the level of uncertainties.

  19. Bedload monitoring under conditions of ultra-high suspended sediment concentrations

    NASA Astrophysics Data System (ADS)

    Liébault, F.; Jantzi, H.; Klotz, S.; Laronne, J. B.; Recking, A.

    2016-09-01

    The bedload response of the Moulin Ravine, a small alluvial system draining a very active Mediterranean badlands landscape entrenched into Jurassic black marls of the Southern French Prealps, has been investigated using an automatic Reid bedload slot sampler. This site is known for its exceptional sediment transport conditions thanks to a long-term monitoring program that started in the late 1980s, revealing a mean annual bedload yield of 2810 t km-2 yr-1, and suspended sediment concentrations (SSCs) during flow events commonly reaching 100 g L-1. With the deployment of the slot sampler, it has been possible to record instantaneous bedload fluxes during 10 s time increments and to investigate bedload response under flow conditions with ultra-high SSCs. Bedload records cover 4 flashy summer flow events induced by heavy convective storms including a 20-yr return period event. Due to the very high SSC conditions these events challenge bedload monitoring. Even if slot sampling has been recognized as insensitive to fine sediments (silts and clays), it has never been tested in such exceptional muddy flow conditions. The bedload slot sampler performed well in such conditions. A flow-invariant proportion of fines (∼15-20%) was captured in the slot sampler during flows. This proportion is equivalent to its content in the active bedload layer during summer flows, suggesting that fines enter the slot embedded with coarse particles. Instantaneous bedload fluxes recorded in the Moulin are amongst the highest hitherto reported values worldwide, providing evidence of the exceptional sediment transport conditions of marly alpine badlands. The dimensionless entrainment threshold is one order of magnitude higher than commonly reported for gravel-bed rivers, likely reflecting the cohesion effect of fines intruded in the channel surface and subsurface.

  20. OTVE turbopump condition monitoring, task E. 5. Final report, October 1988-September 1989

    SciTech Connect

    Coleman, P.T.; Collins, J.J.

    1989-08-01

    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.

  1. Condition monitoring and life-cycle cost design of stay cable by embedded OFBG sensors

    NASA Astrophysics Data System (ADS)

    Lan, C. M.; Ju, Y.; Li, H.

    2011-04-01

    Stay cables are one of the most critical structural components of a cable-stayed bridge. However, stay cables readily suffer from fatigue damage, corrosion damage and their coupled effect. Thus, condition monitoring of stay cables is important to ensure the integrity and safety of a bridge. Glass Fibre Reinforced Polymer Optical Fibre Bragg Grating (GFRP-OFBG) cable, a kind of fibre Bragg grating optical sensing technology-based smart stay cables is used in this study. The application of the smart stay cables on the Tianjin Yonghe Bridge was demonstrated and the vehicle live load effect and fatigue effect of smart stay cables were evaluated based on field monitoring data. Furthermore, the life-cycle cost analysis method of the stay cables is established. Finally, based on the nonlinear reliability index deterioration model, the optimal design of stay cable with different reference period is evaluated.

  2. Photopyroelectric Monitoring of Olive's Ripening Conditions and Olive Oil Quality Using Pulsed Wideband IR Thermal Source

    NASA Astrophysics Data System (ADS)

    Abu-Taha, M. I.; Sarahneh, Y.; Saleh, A. M.

    The present study is based on band absorption of radiation from pulsed wideband infrared (IR) thermal source (PWBS) in conjunction with polyvinylidene fluoride film (PVDF). It is the first time to be employed to monitor the ripening state of olive fruit. Olive's characteristics vary at different stages of ripening, and hence, cultivation of olives at the right time is important in ensuring the best oil quality and maximizes the harvest yield. The photopyroelectric (PPE) signal resulting from absorption of wideband infrared (IR) radiation by fresh olive juice indicates the ripening stage of olives, i.e., allows an estimate of the suitable harvest time. The technique was found to be very useful in discriminating between olive oil samples according to geographical region, shelf life, some storage conditions, and deliberate adulteration. Our results for monitoring oil accumulation in olives during the ripening season agree well with the complicated analytical studies carried out by other researchers.

  3. Multivariate Regression with Calibration*

    PubMed Central

    Liu, Han; Wang, Lie; Zhao, Tuo

    2014-01-01

    We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861

  4. Innovative monitoring campaign of the environmental conditions of the Stibbert museum in Florence

    NASA Astrophysics Data System (ADS)

    Angelini, E.; Civita, F.; Corbellini, S.; Fulginiti, D.; Giovagnoli, A.; Grassini, S.; Parvis, M.

    2016-02-01

    Conservation of ancient metallic artefact displayed inside museums is a complex problem due to the large number of constraints mainly related to the artefacts fruition by people. The development of a simple procedure for monitoring the artefact conservation state promptly highlighting risky conditions without impacting on the normal museum operations could be of interest in the cultural heritage world. This paper describes the interesting results obtained by using a highly sensitive and innovative methodology for evaluating the safety level of the museum indoor areas, and more specifically of the interior of the showcases, with respect to the metallic artefacts. The methodology is based on the use of an innovative smart sensors network and of copper reference samples. The smart sensors network was employed for the continuous monitoring of temperature and relative humidity close to the artefacts, i.e. inside the display showcases. The reference specimens were Cu coated with a 100 nm Cu nanostructured layer put for 1 year in the exhibition rooms inside and outside the showcases and characterised by means of normal imaging, colorimetric and FESEM techniques at regular intervals. The results of the monitoring activity evidenced the higher reactivity to the environmental aggressivity of the nanocoated copper specimen with respect to bulk artefacts and therefore the possibility to use them as alerts to possible corrosion phenomena that may occur to the real artefacts. A proper temperature and relative humidity monitoring inside the showcases and close to each group of artefacts is a powerful though economic and non-invasive way to highlight most of the possible critical display conditions.

  5. Multivariate Data EXplorer (MDX)

    SciTech Connect

    Steed, Chad Allen

    2012-08-01

    The MDX toolkit facilitates exploratory data analysis and visualization of multivariate datasets. MDX provides and interactive graphical user interface to load, explore, and modify multivariate datasets stored in tabular forms. MDX uses an extended version of the parallel coordinates plot and scatterplots to represent the data. The user can perform rapid visual queries using mouse gestures in the visualization panels to select rows or columns of interest. The visualization panel provides coordinated multiple views whereby selections made in one plot are propagated to the other plots. Users can also export selected data or reconfigure the visualization panel to explore relationships between columns and rows in the data.

  6. Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines

    NASA Astrophysics Data System (ADS)

    Er, Poi Voon; Teo, Chek Sing; Tan, Kok Kiong

    2016-02-01

    Moving mechanical parts in a machine will inevitably generate vibration profiles reflecting its operating conditions. Vibration profile analysis is a useful tool for real-time condition monitoring to avoid loss of performance and unwanted machine downtime. In this paper, we propose and validate an approach for sensor placement, selection and fusion for continuous machine condition monitoring. The main idea is to use a minimal series of sensors mounted at key locations of a machine to measure and infer the actual vibration spectrum at a critical point where it is not suitable to mount a sensor. The locations for sensors' mountings which are subsequently used for vibration inference are identified based on sensitivity calibration at these locations moderated with normalized Fisher Information (NFI) associated with the measurement quality of the sensor at that location. Each of the identified sensor placement location is associated with one or more sensitive frequencies for which it ranks top in terms of the moderated sensitivities calibrated. A set of Radial Basis Function (RBF), each of them associated with a range of sensitive frequencies, is used to infer the vibration at the critical point for that frequency. The overall vibration spectrum of the critical point is then fused from these components. A comprehensive set of experimental results for validation of the proposed approach is provided in the paper.

  7. An approach to monitoring HVAC (heating ventilating and air conditioning) technology developments in Japan

    SciTech Connect

    Lewis, P.M.; Ashton, W.B.; McDonald, S.C.

    1987-12-01

    This paper presents a discussion of methods for periodicaly monitoring Japanese advanced technology developments for equipment and components in the heating ventilating and air conditioning (HVAC) industry. The emphasis in the approach recommended is on evaluation of foreign literature - both technical and trade publications - because of both the increasing availability of these materials and the usefulness of information they present. Although not a comprehensive nor completely detailed source of information, HVAC technology literature is an important component of ''scanning the business/technical environmental'' for many purposes. Moreover, despite obstacles in obtaining and translating some important literature, useful knowledge can be obtained from many foreign literature sources for relatively modest costs.

  8. In-Situ Monitoring of Particle Growth at PEMFC Cathode under Accelerated Cycling Conditions

    SciTech Connect

    Redmond, Erin L.; Setzler, Brian P.; Juhas, Pavol; Billinge, Simon J.L.; Fuller, Thomas F.

    2012-10-25

    An in-situ method to measure changes in catalyst particle size at the cathode of a proton exchange membrane fuel cell is demonstrated. Synchrotron X-rays, 58 keV, were used to measure the pair distribution function on an operating fuel cell and observe the growth of catalyst particles under accelerated degradation conditions. The stability of Pt/C and PtCo/C with different initial particle sizes was monitored over 3000 potential cycles. The increase in particle size was fit to a linear trend as a function of cycles. The most stable electrocatalyst was found to be the alloyed PtCo with the larger initial particle size.

  9. Monitoring of forest condition in the Finnish-Russian border region

    SciTech Connect

    Maelkoenen, E.; Lumme, I. ); Tikkanen, E. )

    1994-12-01

    Large industrial and population centers of NW Russia and Estonia are great sources of air pollutants, which is regarded as a threat to the vitality of forests also in Finland. Therefore, the monitoring of forest condition has been set as a central goal of the Finnish-Russian cooperation in the field of environmental protection in near-border districts. Except in the vicinity of emissions sources it has been difficult to distinguish in a scientifically reliable way antropogenic symptoms from natural disturbances and epidemics.

  10. A ground test program to support condition monitoring of a spacecraft attitude control propulsion system

    NASA Technical Reports Server (NTRS)

    Clark, Douglas J.; Lester, Robert W.; Baroth, Edmund C.; Coleman, Arthur L.

    1991-01-01

    The Comet Rendezvous Asteroid Flyby (CRAF) mission involves seven years of flight from 0.6 to 4.57 Astronomical Units (AU), followed by about 915 days of maneuvering around a comet. Ground testing will characterize the very critical attitude control system thrusters' fuel consumption and performance for all anticipated fuel temperatures over thruster life. The ground test program characterization will support flight condition monitoring. A commercial software application hosted on a commercial microcomputer will control ground test operations and data acquisition using a newly designed thrust stand. The data acquisition and control system uses a graphics-based language and features a visual interface to integrate data acquisition and control.