Multi-Sensor Data Fusion Technology Based on BP Neural Network Application in the Coal Mine Equipment Fault Diagnosis

Meng, Xiang Zhong; Liu, Hui Long; Hou, Zi Sheng

doi:10.4028/www.scientific.net/amm.678.238

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2020

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“…The intelligent diagnosis method uses the trained model to predict motor failure. They include backpropagation neural networks [ 10 , 11 ], support vector machines [ 12 , 13 ], etc. This method has low accuracy and requires a large number of samples to train the model.…”

Section: Introductionmentioning

confidence: 99%

Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM

et al. 2020

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To solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wavelet packet to reconstruct the signal. Secondly, the reconstructed signal is input into the improved three-layer LSTM network as a feature vector. The memory characteristics of the LSTM network are used to fully learn the time-series fault feature information in the unsteady state signal, and then, the model is used to diagnose the motor fault. Finally, the feasibility of the proposed method is verified through experiments and can be applied to engineering practice. Compared with the existing motor fault diagnosis method, the improved WP-LSTM diagnosis method has a better diagnosis effect and improves fault diagnosis.

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