Noise corruption always exists in the industrial process. Based on the probabilistic principal component analysis (PPCA) method

a new process monitoring method based on probabilistic independent component analysis (PICA)was proposed

which extends PPCA to the non-Gaussian process. Two statistical quantities (I2 and MR) were constructed for monitoring non-Gaussian and noise information of the process. A case study of the Tennessee Eastman (TE) process showed that the proposed method was feasible and efficient. The process monitoring performance was evidently improved

thus enhancing the reliability and stability of the TE process.