Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis

Abstract

A kernel independent component analysis (KICA) is widely regarded as an effective approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based monitoring methods treat every KIC equally and cannot highlight the useful KICs associated with fault information. Consequently, fault information may not be explored effectively, which may result in degraded fault detection performance. To overcome this problem, we propose a new nonlinear and non-Gaussian process monitoring method using Gaussian mixture model (GMM)-based weighted KICA (WKICA). In particular, in WKICA, GMM is first adopted to estimate the probabilities of the KICs extracted by KICA. The significant KICs embodying the dominant process variation are then discriminated based on the estimated probabilities and assigned with larger weights to capture the significant information during online fault detection. A nonlinear contribution plots method is also developed based on the idea of a sensitivity analysis to help identifying the fault variables after a fault is detected. Simulation studies conducted on a simple four-variable nonlinear system and the Tennessee Eastman benchmark process demonstrate the superiority of the proposed method over the conventional KICA-based method.