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| Research and application of fault detection method for mine belt conveyor |
| Wang Xincheng |
| Shanxi Coal Import and Export Group Hongdong Land Coal Industry Co., Ltd., Linfen 041600， China |
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Abstract Taking DTL100/50/132 belt conveyor in Lucheng Coal Industry as an example, an intelligent fault diagnosis method based on audio wavelet packet decomposition and convolutional neural network ( CNN ) is proposed. The wavelet packet decomposition algorithm is used to decompose the fault audio data into multiple frequency bands, and CNN is used to classify the characteristics of each frequency band to diagnose the fault of belt conveyor. The experimental results show that the diagnosis method has the characteristics of high accuracy, fast speed and strong reliability, and improves the efficiency of fault diagnosis of belt conveyor.
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