Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach
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| Language: | English |
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University of Cincinnati / OhioLINK
2013
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382372576 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1382372576 |
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oai_dc |
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NDLTD |
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English |
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NDLTD |
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Mechanics Residual Clustering Health Monitoring Auto-Associative Neural Network Multi-Regime Sensor Health Gearbox Condition Monitoring |
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Mechanics Residual Clustering Health Monitoring Auto-Associative Neural Network Multi-Regime Sensor Health Gearbox Condition Monitoring Siegel, David Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach |
| author |
Siegel, David |
| author_facet |
Siegel, David |
| author_sort |
Siegel, David |
| title |
Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach |
| title_short |
Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach |
| title_full |
Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach |
| title_fullStr |
Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach |
| title_full_unstemmed |
Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach |
| title_sort |
prognostics and health assessment of a multi-regime system using a residual clustering health monitoring approach |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2013 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382372576 |
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AT siegeldavid prognosticsandhealthassessmentofamultiregimesystemusingaresidualclusteringhealthmonitoringapproach |
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1719435113144516608 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin13823725762021-08-03T06:19:53Z Prognostics and Health Assessment of a Multi-Regime System using a Residual Clustering Health Monitoring Approach Siegel, David Mechanics Residual Clustering Health Monitoring Auto-Associative Neural Network Multi-Regime Sensor Health Gearbox Condition Monitoring Monitoring the health condition of machinery has been an area of research for quite some time. Despites several advancements, the application of conventional signal analysis and pattern recognition methods face several challenges when the operating variables such as load, speed, and temperature vary considerably for the monitored asset. The residual clustering approach addresses the multi-regime monitoring challenge by first modeling the baseline non-linear correlation relationship in the measured signal features and by providing predicted signal features. Calculating the residual signal features allows one to normalize the effect of the operating variables, since one is considering how the response of the system compares with the predicted response based on the baseline behavior. In many instances the degradation signature of a component or system is more pronounced under certain operating conditions. The clustering portion of the residual clustering method specifically addresses the regime dependent signature aspect and bases the health value on the monitoring regime in which the degradation signature is more prevalent. This dissertation work highlights the mathematical framework and provides guidance on the appropriate processing methods for each portion of the approach. From simulation studies and wind speed data, the results highlight that the auto-associative neural network method provides the lowest prediction error when compared with regression, neural network, and principal component analysis methods. The results from this dissertation work also imply that the selection of the clustering algorithm does not significantly affect the calculated health value, and in general, most clustering algorithms appear suitable for detecting the problem using the residual clustering approach. The feasibility of the residual clustering approach is demonstrated in three case studies. For the wind speed sensor health monitoring case study, the residual clustering method provides the most accurate health assessment of the wind speed sensors when compared with the other methods used by the 24 participants in the Prognostics and Health Management 2011 Data Challenge. The residual clustering approach also outperformed other multi-regime health monitoring methods such as a mixture distribution overlap method for the gearbox case study. The residual clustering method was also able to provide an early detection of a problem on the wind turbine rotor shaft with 26 days of advanced warning. The rotor shaft health value using the residual clustering approach had the most monotonic health trend when compared with three other multi-regime health monitoring methods for the wind turbine drivetrain case study. The dissertation work shows that the residual clustering approach is fundamentally sound and should be considered along with the existing methods for multi-regime condition monitoring applications. The method appears to outperform many of the existing methods, and would be an appropriate monitoring algorithm if there is a nominal amount of correlation in the measured signals. Additional refinement of the approach can look into more sophisticated methods for threshold setting along with integrating a feature selection method into the residual clustering framework. In addition, algorithms for diagnosis and remaining useful life estimation for multi-regime condition monitoring applications would also require additional research and development work. 2013 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382372576 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1382372576 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center. |