Abstract
In order to monitor the roller states online running on the belt conveyor, one class of fault diagnosis systems based on audio is studied in this paper. Firstly, the audio data is collected from the belt conveyor by sensors, which is analyzed using the stacked sparse encoders and convolutional neural network. Secondly, the fault features are extracted from the audio data by using spectral clustering algorithm. Finally, a real fault diagnosis system is applied on the belt conveyor working in the coal preparation plant. The running result shows that the fault diagnosis system works very well for rollers fault detection with the accuracy rate 96.7%.
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