Abstract:
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...Show MoreMetadata
Abstract:
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 L_{1} 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.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 25, Issue: 6, June 2014)