Variable Selection-Based Recursive Method for Adaptive Quality-Related Fault Detection

Fault detection (process monitoring) for dynamic and nonlinear industrial processes, which can be viewed as complex systems, has become increasingly important in recent years. Under complex and dynamic operating conditions, traditional multivariate statistical process monitoring (MSPM) methods struggle to effectively capture the evolving behaviors of quality indicators at the system level. In this article, a novel variable selection-based adaptive MSPM method is proposed for quality-related process monitoring. First, an improved part mutual information (PMI) method is proposed for variable selection, categorizing process variables into quality-related and quality-unrelated groups, addressing the shortcomings of the original PMI being able to measure the correlation between only two variables. Second, a novel recursive kernel principal component regression (RKPCR) incorporating the hierarchical model order-reduction strategy is proposed to effectively track quality indicators. This method screens high-quality data for model updates based on distance-based classification and linear approximation, which mitigates model degradation caused by faulty data. In addition, a recursive kernel principal component analysis (RKPCA) method similar to RKPCR is proposed to monitor quality-unrelated faults. Finally, a numerical example and two industrial processes are used to demonstrate the performance of the proposed method.