NDLI: Fault detection and diagnosis using PDF for stochastic distribution systems

Content Provider	IEEE Xplore Digital Library
Author	Li Tao Zheng Weixing Yao Xiuming
Copyright Year	2011
Description	Author affiliation: Department of Automation, North China Electric Power University, Baoding 071003, Hebei, China (Yao Xiuming) \|\| Department of Information and Communication, Nanjing University of Information Science and Technology, 210044, Jiangsu, China (Li Tao) \|\| School of Computing and Mathematics, University of Western Sydney, Penrith NSW 2751, Australia (Zheng Weixing)
Abstract	With the rapid advances in sensor technology, image manipulation and data processing, the feedback measurement information is the stochastic distribution of the system output rather than its value. In this paper, based on the output probability density functions (PDFs) and neural networks, a new fault detection and diagnosis strategy is studied by designing an adaptive observer. The designed observer can not only detect the fault, but also realize the fault diagnosis. Computer simulations are given to demonstrate the efficiency of the proposed approach.
Starting Page	117
Ending Page	121
File Size	120804
Page Count	5
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	fault diagnosis PDF Fault detection Stochastic systems Neural networks Stochastic processes Observers Circuit faults observer design Spline
Content Type	Text
Resource Type	Article

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