NDLI: Fault Detection in Multivariate Signals With Applications to Gas Turbines

Content Provider	IEEE Xplore Digital Library
Author	Bassily, H. Lund, R. Wagner, J.
Copyright Year	1991
Abstract	This paper proposes a fault detection method for multivariate signals. The method assesses whether or not the multivariate autocovariance functions of two independently sampled system signals coincide. If the first signal is known to be sampled from a well-functioning system, then rejection of signal equality is tantamount to concluding that the second signal is sampled from a faulty system. The proposed method is based on the asymptotic properties of the periodogram of multivariate stationary time series and is nonparametric in nature; in particular, there is no need to model the signals under study, an often arduous task. Several natural and synthetic faults were introduced in a Solar Turbines Mercury 50 4.5 MW gas turbine and the resulting compressor delivery pressure and generated electrical power were analyzed. The proposed method capably detected all faults.
Sponsorship	IEEE Signal Processing Society
Starting Page	835
Ending Page	842
Page Count	8
File Size	1060608
File Format	PDF
ISSN	1053587X
Volume Number	57
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Signal detection Turbines Autoregressive processes Principal component analysis Predictive models Power system modeling Electrical fault detection Frequency domain analysis Hidden Markov models stationary time series Autocovariances fault detection spectral analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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