NDLI: Unsupervised neural network for fault detection and classification in dynamic systems

Content Provider	IEEE Xplore Digital Library
Author	Xiaoqin Pei Chowdhury, F.N.
Copyright Year	1999
Description	Author affiliation: Center for Adv. Comput. Studies, Univ. of Southwestern Louisiana, Lafayette, LA, USA (Xiaoqin Pei)
Abstract	We present recent results of using a Kohonen neural network to detect and classify faults occurring in a dynamic system. The measured outputs from the system are first used in a Kalman filter to generate residual vectors that serve as fault indicators. As the residuals are generated they can be sent one-by-one to the Kohonen network, both the Kalman filter and the Kohonen network operating in real time. The Kohonen network detects and categorizes the fault, since the residual vectors serve as signatures for different types of faults. The Kohonen network starts with a few pre-designated categories, each category representing a fault type. As more and more residual vectors become available, the Kohonen network opens new categories for residuals that do not have a good enough match with any of the existing categories. The concept is illustrated by an application example that uses actual fault data commercially recorded by the utilities in Texas.
Starting Page	640
Ending Page	645
File Size	491089
Page Count	6
File Format	PDF
ISBN	078035446X
DOI	10.1109/CCA.1999.806727
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-08-22
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Fault detection Intelligent networks Unsupervised learning Electrical fault detection Supervised learning Neurons System identification Estimation theory Testing
Content Type	Text
Resource Type	Article

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