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

作　　者：王新成 Wang Xincheng(Shanxi Coal Import and Export Group Hongdong Land Coal Industry Co.,Ltd.,Linfen 041600,China)

机构地区：[1]山西煤炭进出口集团洪洞陆成煤业有限公司,山西临汾041600

出　　处：《煤炭与化工》2022年第3期102-104,共3页Coal and Chemical Industry

摘　　要：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.

关 键 词：带式输送机 音频监测 故障检测 

分 类 号：TD52[矿业工程—矿山机电]