摘要
对轴承故障诊断方法、采煤机滚筒轴承组成、故障类型进行分析,运用试验的方法对轴承实时数据进行采集和振动信号分解,建立GA-BP神经网络故障诊断模型,对轴承故障进行诊断分析。结果表明,轴承故障识别准确率达到99.8%,为采煤机滚筒轴承故障诊断及使用寿命延长提供了有益的借鉴。
The bearing fault diagnosis method,the composition and fault type of the shearer drum bearing have been analyzed,the real-time data of the bearing have been collected by using experimental methods and the vibration signal has been decomposed,a GA-BP neural network bearing fault diagnosis model has been established to diagnose and analyze the bearing fault.The results show that the accuracy rate of bearing fault identification reaches 99.8%,which provides a useful reference for fault diagnosis of shearer drum bearing and prolonging the service life.
作者
李合菊
李素叶
魏珑
吴晓燕
Li Heju;Li Suye;Wei Long;Wu Xiaoyan(Jinan Vocational College,Jinan 250002,China;Shandong Jianzhu University,Jinan 250101,China;Laiwu Vocational and Technical College,Jinan 271199,China)
出处
《煤矿机械》
2024年第8期184-186,共3页
Coal Mine Machinery
