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Major catastrophic failures in large scale engineering systems (e.g., aircraft, power plants and turbo-machinery) can possibly be averted if the malignant anomalies are detected at an early stage. This dissertation experimentally validates a novel method called Symbolic Time Series Analysis(STSA) for anomaly detection in electromechanical systems, derived from time series data of pertinent measured variable(s). In this dissertation, the performance of this anomaly detection method is compared with that of other existing pattern recognition techniques from the perspectives of early detection of fatigue damage in Al-2024. The experimental apparatus, on which the anomaly detection method is tested, is a multi-degree of freedom mass-beam structure excited by oscillatory motion of two electromagnetic shakers. The evolution of fatigue crack damage at one of the failure sites is detected from STSA of the pertinent sensor signal. Industrial Application-The dissertation presents STSA of bearing acceleration derived from a dynamic simulation model for detection and estimation of parametric changes in flexible disc/diaphragm couplings due to angular misalignment between shafts.
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Amol Khatkhate received the Ph.D. degree in Mechanical Engineering in August 2006 from Pennsylvania State University,USA.He also received his Masters in Electrical Engineering in May 2005 from PennState,USA.His research interests include fiber optics,pattern recognition and structural health monitoring (SHM) of composite aircraft structures.
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- PublisherVDM Verlag
- Publication date2009
- ISBN 10 3639136764
- ISBN 13 9783639136760
- BindingPaperback
- Number of pages156
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Book Description Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Major catastrophic failures in large scaleengineering systems (e.g., aircraft, power plants andturbo-machinery) can possibly be averted if themalignant anomalies are detected at an early stage.This dissertation experimentally validates a novelmethod called Symbolic Time Series Analysis(STSA) foranomaly detection in electromechanical systems,derived from time series data of pertinent measuredvariable(s).In this dissertation, the performance ofthis anomaly detection method is compared with thatof other existing pattern recognition techniques fromthe perspectives of early detection of fatigue damagein Al-2024. The experimental apparatus, on which theanomaly detection method is tested, is a multi-degreeof freedom mass-beam structure excited by oscillatorymotion of two electromagnetic shakers. The evolutionof fatigue crack damage at one of the failure sitesis detected from STSA of the pertinent sensor signal.Industrial Application-The dissertation presents STSAof bearing acceleration derived from a dynamicsimulation model for detection and estimation ofparametric changes in flexible disc/diaphragmcouplings due to angular misalignment between shafts. 156 pp. Englisch. Seller Inventory # 9783639136760
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Book Description Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khatkhate AmolAmol Khatkhate received the Ph.D. degree in MechanicalnEngineering in August 2006 from Pennsylvania StatenUniversity,USA.He also received his Masters in ElectricalnEngineering in May 2005 from PennState,USA.His research. Seller Inventory # 4960773
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