Fault diagnosis for a class of nonlinear uncertain hybrid systems

This paper presents a fault diagnosis architecture for a class of hybrid systems with nonlinear uncertain time-driven dynamics, measurement noise, and autonomous and controlled mode transitions. The proposed approach features a hybrid estimator based on a modified hybrid automaton framework. The fault detection scheme employs a filtering approach that attenuates the effect of the measurement noise and allows tighter mode-dependent thresholds for the detection of both discrete and parametric faults while guaranteeing no false alarms due to modeling uncertainty and mode mismatches. Both the hybrid estimator and the fault detection scheme are linked with an autonomous guard events identification (AGEI) scheme that handles the effects of mode mismatches due to autonomous mode transitions and allows effective mode estimation. Finally, the fault isolation scheme anticipates which fault events may have occurred and dynamically employs the appropriate isolation estimators for isolating the fault by calculating suitable thresholds and estimating the parametric fault magnitude through adaptive approximation methods. Simulation results from a five-tank hybrid system illustrate the effectiveness of the proposed approach.(c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a fault diagnosis architecture for a class of hybrid systems with nonlinear uncertain dynamics, measurement noise, and mode transitions. The proposed approach utilizes a hybrid estimator based on a modified automaton framework, a filtering approach for fault detection, and an autonomous guard events identification scheme. Additionally, it includes a fault isolation scheme that anticipates potential fault events and employs suitable isolation estimators. Simulation results demonstrate the effectiveness of the proposed approach.