MK-DCCA Based Fault Diagnosis for Incipient Fault in Nonlinear Dynamic Processes

Incipient fault detection is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Component Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent monitoring performance in detecting incipient faults in nonlinear dynamic systems. Furthermore, fault identification experiments were conducted, validating the high accuracy of the accompanying contribution graph method.