Fault diagnosis method of ship power circuit based on neural network
In order to effectively filter out the noise and interference in the signal of ship power circuit,extract the use-ful fault characteristics,and accurately diagnose various unknown fault types in the complex and variable operating environ-ment,a fault diagnosis method of ship power circuit based on neural network is studied.The stack sparse auto encoder is used to extract useful circuit fault features from ship power circuit signals,and the noise and interference in circuit signals are filtered by its sparsity to reduce the redundancy between fault features.The structure of probabilistic neural network is optim-ized by K-means algorithm.In the optimized probabilistic neural network,useful fault features are input and the fault dia-gnosis results of ship power circuit are output.According to its powerful online learning ability,its adaptability to unknown fault diagnosis is improved.Experimental results show that this method can extract the fault characteristics of ship power cir-cuit effectively.The method can accurately diagnose circuit faults under different noise intensity operating environment.
neural networkship power circuitfault diagnosisautoencoderK-means algorithmprobabilistic neural network
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