Fault Diagnosis Based on K-Means Clustering and PNN

Authors:
Dongsheng Wu;Qing Yang;Feng Tian;Dong Xu Zhang
Affiliations:
-;-;-;-
Venue:
ICINIS '10 Proceedings of the 2010 Third International Conference on Intelligent Networks and Intelligent Systems
Year:
2010

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Fault detection and identification spanning multiple processes by integrating PCA with neural network

Applied Soft Computing

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Abstract

This paper presents the development of an algorithm based on K-Means clustering and probabilistic neural network (PNN) for classifying the industrial system faults. The proposed technique consists of a preprocessing unit based on K-Means clustering and probabilistic neural network (PNN). Given a set of data points, firstly the K-Means algorithm is used to obtain K-temporary clusters, and then PNN is used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, K-Means and PNN are applied to diagnose the faults in TE Process. Simulation studies show that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions and the result is also reliable.