Research status and prospect of fault diagnosis of China’s coal mine machines under background of intelligent mine
FAN Hongwei1,2,ZHANG Xuhui1,2,CAO Xiangang1,2,WAN Xiang1,2,YANG Yiqing1
1.School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China;
2.Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, China
Abstract：At present, coal is still the main energy source of China.Coal mine underground environment is complex and bad, which makes coal mine equipment malfunction frequently, and poses a serious threat to mining safety.Mechanical fault diagnosis technology is mainly based on vibration, including dynamics and fault mechanism, signal processing and feature extraction, intelligent diagnosis based on data.Study of fault mechanism provides a basis for signal feature extraction and intelligent diagnosis; it mainly studies vibration law of bearing, gear and mechanical system under fault condition, especially frequency constitution.The purpose of signal processing algorithms is to extract components reflecting fault information from measured signals, which includes spectrum analysis, wavelet analysis, and empirical mode decomposition.Data-based intelligent diagnosis has developed rapidly, which is mainly used to classify, cluster and regress monitoring data, according to characteristics of data.There are support vector machine, shallow neural network and deep learning algorithms.Population intelligent algorithms are often used to optimize parameters of these methods.Research on mechanical fault diagnosis of coal mine equipment is lagging behind, and more efforts should be made in basic theory, algorithm development and engineering application to support China’s intelligent mine and green, safe and efficient development of coal industry.
樊红卫1,2,张旭辉1,2,曹现刚1,2,万翔1,2,杨一晴1. 智慧矿山背景下我国煤矿机械故障诊断研究现状与展望[J]. 振动与冲击, 2020, 39(24): 194-204.
FAN Hongwei1,2,ZHANG Xuhui1,2,CAO Xiangang1,2,WAN Xiang1,2,YANG Yiqing1. Research status and prospect of fault diagnosis of China’s coal mine machines under background of intelligent mine. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(24): 194-204.
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