A novel integrated framework for fault diagnosis with application to process safety

Abstract

A novel integrated framework for Fault Detection and Isolation(FDI) is proposed, with applications to process safety, by sequentially integrating model-free(MFA) and model-based(MBA) approaches. The MFA includes Limit Checking/Visual and Plausibility analysis, Artificial Neural Network and Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System. The MBA uses a Linear Parameter-Varying model to handle a wide class of generally-nonlinear physical systems. The adaptive Kalman filter(KF) residuals are used for FDI in the MBA. Novel emulators, cascaded with the system during off-line data acquisition, system identification and Bayes’measure of belief computation for each FDI scheme, have their parameters perturbed at each operating point, to mimic unforeseen operational scenarios, thus covering all operating regions. Critical information about the presence/absence of a fault is quickly gained via the faster FDI scheme. A more accurate subsystem’s status is unfolded sequentially by the slower FDI scheme. The final decision on the fault status is obtained using a weighted Bayes classifier fusion scheme meeting the critical requirements of high(low) probability of correct decision (false alarm). Implications of FDI in process safety/environment protection are discussed. This framework is successfully evaluated on simulated and physical systems, including benchmarked laboratory-scale two-tank system, by detecting and isolating sensor, actuator and leakage faults.