Abstract The roller is an important part of the belt conveyor in sand carrier at sea. A good fault diagnosis method of the rollers provides an effective guarantee for the system’s optimal operation. In this paper, a novel intelligent fault diagnosis method for rollers is proposed by using audio wavelet packet decomposition and Convolutional Neural Networks (CNN). Firstly, the wavelet packet decomposition algorithm is used to decompose the audio data of the rollers into several frequency bands. Secondly, the lowest frequency data are adjusted under consideration of the excessive energy proportion of the low frequency data. Then, CNN is used to classify the features of each frequency band to diagnose rollers’ faults. The experiment shows that the diagnosis method has high accuracy, high speed and strong robustness, which greatly improves the efficiency of fault diagnosis of rollers of sand carrier.

中文翻译：

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一种基于音频的砂轮带式输送机托辊故障智能诊断方法

摘要 滚筒是海上运砂带式输送机的重要组成部分。良好的压路机故障诊断方法为系统的优化运行提供了有效保障。本文利用音频小波包分解和卷积神经网络（CNN）提出了一种新型的滚子故障智能诊断方法。首先利用小波包分解算法将滚轮的音频数据分解成若干个频段。其次，考虑到低频数据能量占比过大，调整最低频率数据。然后，利用CNN对各个频段的特征进行分类，从而诊断出滚筒的故障。实验表明，该诊断方法准确率高、速度快、鲁棒性强，