This paper presents a two-layer multi-model gas path fault diagnosis method for gas turbines that includes a fault detection layer and a fault isolation layer. A health model and a gas path fault model based on a back propagation neural network are used for the real-time estimation of the output parameters of a gas turbine in the fault detection layer and the output parameter residual in the fault isolation layer, respectively. A fault detection algorithm is proposed based on fuzzy inference, and the fuzzy membership function of the output parameters residual is realized using data statistics. A similarity distance method is used to realize fault isolation, and a fault probability algorithm based on the Mahalanobis distance is presented. Finally, the proposed method is verified by a three-shaft gas turbine simulation platform, and the simulation test results show that the two-layer multi-model gas path fault diagnosis method can detect and isolate the gas path fault accurately with a low calculation cost and good extensibility.

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