| Static and dynamic novelty detection methods for jet engine health monitoring. | |
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MedLine Citation:
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PMID: 17255049 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation-maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter. |
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Authors:
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Paul Hayton; Simukai Utete; Dennis King; Steve King; Paul Anuzis; Lionel Tarassenko |
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Publication Detail:
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Type: Journal Article; Review |
Journal Detail:
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Title: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Volume: 365 ISSN: 1364-503X ISO Abbreviation: Philos Trans A Math Phys Eng Sci Publication Date: 2007 Feb |
Date Detail:
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Created Date: 2007-01-26 Completed Date: 2007-03-27 Revised Date: 2013-04-24 |
Medline Journal Info:
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Nlm Unique ID: 101133385 Medline TA: Philos Trans A Math Phys Eng Sci Country: England |
Other Details:
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Languages: eng Pagination: 493-514 Citation Subset: IM |
Affiliation:
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Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Aircraft
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instrumentation* Algorithms Computer Simulation Construction Materials / analysis* Engineering / instrumentation, methods Equipment Design Equipment Failure Analysis / instrumentation, methods* Maintenance / methods Materials Testing / methods* Models, Theoretical* Signal Processing, Computer-Assisted Transducers Vibration |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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