A Two-Stage Fault Detection and Isolation Platform for Industrial Systems Using Residual Evaluation

Fault detection and isolation (FDI) is an important part of modern industrial systems, and plays a vital role in maintainability, safety, and reliability of process. We propose a novel FDI architecture based on a predictive model for fault-free process. We use the least-square support vector machine for identifying a nonlinear system and detecting its faults that may occur. Wavelet analysis on residual is used for fault isolation. An average accuracy of 95% has been achieved for all the abrupt faults of the well-known development and application of methods for actuator diagnosis in industrial control systems benchmark.