Fault Detection and Isolation for Time-Varying Processes Using Neural-Based Principal Component Analysis

This paper introduces a new adaptive framework for fault detection and diagnosis using neural-based PCA. This framework addresses the limitations of traditional PCA in handling time-varying processes. The adaptive framework updates the correlation matrix recursively, allowing it to adapt to the natural time-varying behavior of processes. It also recursively determines the number of principal components and the confidence limits for three process monitoring statistics (T2, Q, and the combined index φ). To diagnose faults, four different types of contribution plots are used as follows: complete decomposition contributions (CDC), partial decomposition contributions (PDC), diagonal-based contributions (DBCs), and reconstruction-based contributions (RBCs). The evaluation through three simulation studies—including a numerical example, the continuous stirred tank reactor (CSTR) process, and water resource recovery facilities (WRRFs)—demonstrates that the combined statistics provided superior fault detection and diagnosis performance compared with individual statistics. Additionally, the study of the isolation method shows that no single method can definitively be claimed as superior. Overall, our study highlights the strength and versatility of neural-based PCA for detecting and diagnosing faults in dynamic processes.