Rapid oscillation fault detection for distributed system via deterministic learning

In this paper, a rapid detection and isolation scheme for oscillation faults in a distributed nonlinear system is proposed based on a recent result on deterministic learning (DL) theory. The distributed nonlinear system considered is modeled as a set of interconnected subsystems. Firstly, a local learning and merging method based on DL is proposed to obtain knowledge of the unknown interconnections and the fault functions. Secondly, by utilizing 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. Thirdly, 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 by 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.