Recently

the statistic pattern analysis (SPA) has been used with widespread applications in the field of fault detection. Its essence is to use data statistics matrix for process monitoring instead of the original data matrix. However

SPA lacks reasonable method in choosing the statistics variables

also complex nonlinear interactions exist among these statistics variables. As a result

fault detection cannot be processed by using ordinary principal component analysis (PCA) algorithm. In order to solve these problems

a new minimum sufficient statistics pattern analysis (MSSPA) fault detection method is proposed. This method first eliminates the correlations among variables by performing an orthogonal transformation of the raw data matrix

and then estimates the probability density function of the single variables or joint probability density function of multiple variables

so as to acquire the minimum sufficient statistic of original data

and construct the statistic matrix with it. The introduction of minimum sufficient statistics is also beneficial to handle the problem of non-Gaussian distribution of the raw data. Finally

the feasibility and validity of this method for fault detection are verified by testing on the Tennessee Eastmann (TE) process.