Author(s): ZHANG Hanyuan, TIAN Xuemin, DENG Xiaogang, College of Information and Control Engineering, China University of Petroleum
Pages: 4503-4508
Year: 2013 Issue:  12
Journal: Journal of Chemical Industry and Engineering(China)
Keyword:  statistics pattern analysis; PCA similarity factor; fault detection; fault identification;
Abstract: The traditional principal component analysis(PCA)similarity factor method does not make full use of the higher-order statistics of the process data,which results in degraded fault identification performance.In order to solve this problem,a statistics pattern analysis(SPA)similarity factor method is proposed in this paper.Firstly,the original process data are transformed into the statistics space by using the SPA.Then,the PCA is adopted to obtain the principal component directions in the statistics space.Finally,the similarity between the principal components is calculated to identify faults.Simulation results on the continuous stirring tank reactor(CSTR)process show that the proposed SPA similarity factor method is more effective than the traditional PCA similarity factor method in terms of identifying faults.