文献类型：会议论文

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

作者：Wang, L; Yu, JS

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

会议论文集：1st International Conference on Natural Computation (ICNC 2005)

会议日期：AUG 27-29, 2005

会议地点：Changsha, PEOPLES R CHINA

语种：英文

摘要：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.