摘要
为解决露天矿带式输送机托辊轴承发生故障识别精度低的问题,提高故障诊断精确性以及效率,提出以优化的优化变分模态分解的方法为基础的混沌粒子群优化算法优化变分模态分解的托辊轴承故障信号检测方法。首先,应用CPSO的出色全局寻优特性,精确锁定变分模态分解算法的最适参数设定,实现对VMD的有效调参;然后,运用调参后的VMD技术处理振动数据,从中精准提取特定的频带信号成分;最后,配合稀疏最大谐波噪声比解卷积(SMHD)技术深度净化上述频带信号,显著增强带式输送机托辊轴承故障特征的辨识准确度。结果表明:CPSO对VMD改进相对于其余的VMD优化算法具有更加优越的性能;经过CPSO优化后的VMD算法结合SMHD对于滚动轴承在复杂工况下能够成功确认滚动轴承内圈以及外圈不易识别的具体故障点,并能判定轴承的具体损坏形态。
The roller bearings of open pit belt conveyors face problems of low fault identification accuracy.To improve the accuracy and efficiency of fault diagnosis,a fault signal detection method of roller bearings with CPSO algorithm based on OVMD was proposed.Firstly,the excellent global optimization characteristics of CPSO were utilized,and the optimal parameter setting of the variational mode decomposition(VMD)algorithm was precisely locked to achieve effective parameter tuning of VMD.Then,VMD technology after parameter tuning was used to process the vibration data,and specific frequency band signal components were accurately extracted from the vibration data.Finally,the sparse maximum harmonic noise ratio deconvolution(SMHD)technology was used to purify the above frequency band signals,which significantly enhanced the identification accuracy of the fault characteristics of roller bearings of belt conveyors.The results show that CPSO has better performance for VMD improvement than other VMD optimization algorithms.The VMD algorithm after CPSO optimization combined with SMHD can successfully identify the specific fault points of the inner and outer rings of the rolling bearings under complex working conditions and determine the specific damage forms of the bearings.
作者
马鹏飞
杨海鸥
王世龙
刘磊
辛昊天
MA Pengfei;YANG Haiou;WANG Shilong;LIU Lei;XIN Haotian(Open Pit Coal Mine,Guoneng Baolixile Energy Co.,Ltd.,Hulunbuir Inner Mongolia 021008,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2024年第S01期156-164,共9页
China Safety Science Journal
作者简介
马鹏飞(1986—),男,辽宁辽阳人,本科,工程师,主要从事煤矿设备管理维修智能化信息化方面的工作。E-mail:11550151@chnenergy.com.cn;辛昊天,工程师
