矿井带式输送机运行故障检测方法研究与应用

Research and application of fault detection method for mine belt conveyor

以陆成煤业DTL100/50/132型带式输送机为例,提出了一种基于音频小波包分解和卷积神经网络(CNN)的智能故障诊断方法。采用小波包分解算法将故障的音频数据分解为多个频段,利用CNN对每个频带的特征进行分类,诊断带式输送机故障。实验结果表明,该诊断方法具有准确率高、速度快、可靠性强等特点,提高了带式输送机的故障诊断效率。

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.