关键词: ICA混合模型, 多工况过程, 后验概率, 传递熵, 贡献图

Abstract:

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.

Key words: ICA mixture model, multimode process, posterior probability, transfer entropy, contribution plot