IMPROVED PCA WITH APPLICATION TO PROCESS MONITORING AND FAULT DIAGNOSIS
Principal component analysis (PCA) is an effective method to extract relationships between variables and thus has been widely applied to multivariate statistical process monitoring and fault diagnosis. In this paper,an improved PCA is presented which uses principal-component-related variable residual (PVR) statistic to replace Q -statistic in the conventional PCA. The improved PCA can avoid the conservation of Q statistical test and provide more explicit information about the process conditions.As a result,the root cause that violates the Hotelling T 2 test but still satisfies Q test can be unambiguously identified,which is impossible in the conventional PCA.Then a simulated double-effect evaporator is monitored and diagnosed by using this proposed method and comparisons with the conventional PCA are made.The simulation result shows that the improved PCA is more sensitive to weak process changes and has an enhanced fault diagnosing performance.