Deterministic learning and fault diagnosis for nonlinear oscillation system

The diagnosis of faults is one of the important tasks in engineering systems. In this paper, based on the recent results on deterministic learning (DL) theory and rapid dynamical pattern recognition, a rapid fault diagnosis scheme is proposed for nonlinear oscillation systems. Firstly, a neural network bank for fault detection and isolation (FDI) is established through DL. Secondly, a mechanism for rapid FDI is presented, by which a fault occurred can be detected and isolated by patten recognition. Simulation studies are included to demonstrate the effectiveness of the proposed approach.

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