For the nature of multimode and non-Gaussian distribution in industrial process data

a fault detection method was proposed for multimode processes based on independent component analysis mixture model (ICAMM). In this method

Bayesian inference and independent component analysis (ICA) were combined to create a probability mixture model; the mode classification of each observation by Bayesian inference and ICA model parameters' setting were accomplished simultaneously; and the global monitoring statistics were established within the Bayesian framework to monitor real-time process changes. In order to solve the problem that traditional variable contribution plot could not indicate the relationships of information transmission among fault variables after fault detection

a fault recognition method for multimode processes was further proposed on the basis of information transfer contribution plot. Three steps were developed in the fault recognition method

including the calculation of variable contributions to the independent component analysis mixture model

the determination of cause-and-effect relationships of fault variables through variable transfer capability and the nearest neighbor transfer entropy

and the finding of fault source variables and fault propagation process. Simulation study on a numerical example and continuous stirring tank reactor (CSTR) system showed effectiveness of the proposed approach.