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%.

中文翻译：

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基于音频的带式输送机滚筒故障诊断

摘要 为了在线监测带式输送机上运行的滚筒状态，本文研究了一类基于音频的故障诊断系统。首先，通过传感器从带式输送机收集音频数据，使用堆叠稀疏编码器和卷积神经网络对其进行分析。其次，利用谱聚类算法从音频数据中提取故障特征。最后，在选煤厂的带式输送机上应用了真实故障诊断系统。运行结果表明，故障诊断系统对辊子故障检测效果良好，准确率达96.7%。