A Bayesian Framework for Fault Diagnosis of Hybrid Linear Systems

Fault diagnosis is crucial for guaranteeing safe, reliable and efficient operation of modern engineering systems. These systems are typically hybrid. They combine continuous plant dynamics described by continuous-state variables and discrete switching behavior between several operating modes. This paper presents an integrated approach for online tracking and diagnosis of hybrid linear systems. The diagnosis framework combines multiple modules that realize the hybrid observer, fault detection, isolation and identification functionalities. More specifically, a Dynamic Bayesian Network (DBN)-based particle filtering (PF) method is employed in the hybrid observer to track nominal system behavior. The diagnostic module combines a qualitative fault isolation method using hybrid TRANSCEND, and a quantitative estimation method that again employs a DBN-based PF approach to isolate and identify abrupt and incipient parametric faults, discrete faults and sensor faults in a computationally efficient manner. Finally, simulation and experimental studies performed on a hybrid two-tank system demonstrate the effectiveness of this approach.