Treatment Methods for the Multiple Electromechanical Faults of Brushless DC Motors Based on the Radial Basis Function Neural Network
In order to solve the problem of the fault detection and classification of brushless DC motors,this paper proposes a new diagnostic method that can be used to locate various electromechanical faults such as the inter-turn fault of the stator,and the dynamic and static imbalance of the rotor.Combined with the current signal,motor torque and speed information of the brushless DC motor,it extracts fault features by the wavelet packet transform and uses them as the input data of radial basis function neural networks.By the particle swarm optimization algo-rithm and genetic algorithm,it updates the weights of the neural network,which improves the efficiency and flex-ibility of algorithms.Finally,it verifies the effectiveness of the proposed method by comparing the combined results of different neural networks and optimization methods.
Brushless DC motorWavelet packet transformNeural networkParticle swarm optimization algorithmGenetic algorithm
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