CAJ | 학술논문

滚动轴承是矿用皮带机的重要零部件，直接决定着皮带机的运转状况。因此，对其开展故障诊断研究有重要的理论和现实意义。研究了人工神经网络的原理、结构和学习算法，并将该网络应用于皮带机滚动轴承的故障诊断中。首先采集不同类型的滚动轴承故障信号，并对信号进行预处理。然后对神经网络进行训练，当训练误差满足设定要求时，训练完成。最后，利用训练成熟的神经网络对滚动轴承进行故障诊断。实验结果表明神经网络技术可以快速、准确地诊断出皮带机滚动轴承的故障类型。
곤동축승시광용피대궤적중요령부건，직접결정착피대궤적운전상황。인차，대기개전고장진단연구유중요적이론화현실의의。연구료인공신경망락적원리、결구화학습산법，병장해망락응용우피대궤곤동축승적고장진단중。수선채집불동류형적곤동축승고장신호，병대신호진행예처리。연후대신경망락진행훈련，당훈련오차만족설정요구시，훈련완성。최후，이용훈련성숙적신경망락대곤동축승진행고장진단。실험결과표명신경망락기술가이쾌속、준학지진단출피대궤곤동축승적고장류형。
Roller element bearing is an important part of mine belt conveyor,which directly determining working condition of the belt. Therefore,there are important theoretical and practical significance in studying fault diagnosis of the roller element bearing. The learning algorithm,structure and principle of artificial neural network were studied,and the network was applied in fault diagnosis of roller element bearing of the belt conveyor. Firstly,fault signals of different types of roller bearings were collected,and the signals were pre-processed. Then,the neural network was trained,when the training errors met settings,the training was completed. Finally,the roller element bearing was fault diagnosed by the well trained neural network. Experimental results show that the neural network tech-nique can diagnose rapidly and precisely the fault types of the bearing of belt conveyor.