Fault feature selection based on modified binary PSO with mutation and its application in chemical process fault diagnosis

Authors:
Ling Wang;Jinshou Yu
Affiliations:
Research Institution of Automation, East China University of Science & Technology, Shanghai, China;Research Institution of Automation, East China University of Science & Technology, Shanghai, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

In large scale industry systems, especially in chemical process industry, large amounts of variables are monitored. When all variables are collected for fault diagnosis, it results in poor fault classification because there are too many irrelevant variables, which also increase the dimensions of data. A novel optimization algorithm, based on a modified binary Particle Swarm Optimization with mutation (MBPSOM) combined with Support Vector Machine (SVM), is proposed to select the fault feature variables for fault diagnosis. The simulations on Tennessee Eastman process (TEP) show the BMPSOM can effectively escape from local optima to find the global optimal value comparing with initial modified binary PSO (MBPSO). And based on fault feature selection, more satisfied performances of fault diagnosis are achieved.