Fault diagnosis method using learning case-based reasoning for Tennessee-Eastman process
YAN Ai-jun
WANG Ying-jie
WANG Dian-hui
Abstract：To diagnose the fault in the Tennessee-Eastman (TE) process more accurately, a learning pseudo metric (LPM)-based case retrival method is proposed to replace distance measure retrieval method and a case-based reasoning (CBR)fault diagnosis model of TE process is established.Firstly,the LPM metrics are established to train the LPM model. Then,the similarity between the target case and each source case is measured to find the same type of cases as the target case. Next,the solution of the target case is obtained based on the majority of reuse principle. Finally,the running data of TE process are used to carry out a performance test and a comparison experiment. The results show that the proposed LPM-based CBR method is superior to traditional CBR,back-propagation(BP)neural network and support vector machine method and significantly improves the accuracy of the fault diagnosis.It has a promotional value for fault diagnosis in the actual chemical process.
Keywords：TE processfault diagnosiscase-based reasoninglearning pseudo metriccase retrieval
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1179-1184 )