Rapid Oscillation Fault Detection and Isolation for Distributed Systems via Deterministic Learning

In this paper, a rapid detection and isolation scheme for oscillation faults in a distributed nonlinear system is proposed. The distributed nonlinear system considered is modeled as a set of interconnected subsystems. First, a local learning and merging method based on deterministic learning theory is proposed to obtain knowledge of the unknown interconnections and the fault functions. Second, using learned knowledge, a bank of consensus-based dynamical estimators are constructed for each subsystem, and average L₁ norms of the residuals are generated to make the detection and isolation decisions. Third, a rigorous analysis for characterizing the detection and isolation capabilities of the proposed scheme is given. The attraction of the intelligence fault diagnosis approach is to give a fast response to faults using the learned knowledge and processing huge data in a dynamical and distributed manner. Simulation studies are included to demonstrate the effectiveness of the approach.