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In this paper, due to no clear match relationship between the fault symptom and fault type of the main fan in coal mine, an improved particle swarm wavelet neural network model is used to diagnose the fault of the main fan in coal mine. Firstly, the vibration signal characteristics of 5 kinds of common faults of ventilators are introduced, and the feature vectors of faults are extracted by multi-scale wavelet transform. Then wavelet neural network is used for fault diagnosis, and the selection of initial parameters of neural network is optimized by the improved particle swarm optimization algorithm. The training speed of wavelet neural network is increased and the training precision is improved to get better fault diagnosis results by adding weight factor, steering operator and random disturbance. The simulation results show that the improved particle swarm wavelet neural network model has better stability, and the accuracy is improved by 2% by comparing with the traditional wavelet neural network. This method can be better applied to the diagnosis of ventilator.
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