Neural Networks-based Fault Detection and Isolation of Nonlinear Systems

I.S. Lee. J.T. Kim, J.W. Lee, Y.J. Lee, and K.Y. Kim (Korea)


Fault detection, isolation, nonlinear system, neural network, statistical method


This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output

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