Sensor Bias Fault Diagnosis for a Class of Nonlinear Uncertain Hybrid Systems

Abstract

This paper presents a sensor bias fault diagnosis approach for a class of hybrid systems with nonlinear uncertain discrete-time dynamics, measurement noise, and autonomous and controlled mode transitions. The proposed approach uses an observer based on a modified hybrid automaton framework and a fault detection scheme that employs a filtering method tighter mode-dependent thresholds for the detection of sensor faults (even with small magnitude). An autonomous guard events identification (AGEI) module is also developed and linked with both the fault detection scheme and the hybrid observer to eliminate any false alarms due to autonomous mode transitions and allow effective mode estimation. Finally, an adaptive sensor fault estimation scheme is included, which is activated once a fault is detected to isolate and estimate the sensor bias fault magnitude.