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.