摘要
早期滚动轴承故障较难诊断,且特征模式分解(FMD)输入参数滤波器长度L和模态分量个数n较难准确选择。因此,提出基于鹈鹕优化算法(POA)优化FMD参数的滚动轴承早期故障诊断方法。该方法以全体峭度的指标作为适应度函数,通过POA优化算法获取FMD优良参数组合,并且结合包络谱分析实现故障诊断。使用该方法对滚动轴承早期故障仿真信号和实验信号进行分析,结果表明:该方法通过优化FMD参数,可以从包络谱中得到故障特征频率及其倍频突出的幅值,进而诊断出滚动轴承早期故障类别;与基于固有时间尺度分解(ITD)和基于最小熵解卷积(MED)方法相比,包络谱中的故障特征频率及其倍频幅值更突出,在滚动轴承早期故障诊断中具有一定的应用前景和价值。
Early rolling bearing faults are challenging to diagnosed due to the difficulty in accurately selecting the input parameters of feature mode decomposition(FMD)filter length L and the number of modal components n.To address this problem,an early fault diagnosis of rolling bearings based on pelican optimization algorithm(POA)optimised FMD parameters is proposed.The method utilizes the ensemble kurtosiss index as the fitness function to achieve optimal parameter combinations of FMD,and integrates with envelope spectrum analysis for fault diagnosis.Applying this method to the rolling bearing early fault simulation signals and experimental signals,the results show that it can accurately extract fault characteristic frequency and its multiplicative amplitude from the envelope spectrum by optimizing the FMD parameters,identifying the early fault classification in rolling bearing.Compared with the methods based on intrinsic time scale decomposition(ITD)and minimum entropy deconvolution(MED),the proposed approach demonstrates superior performance in extracting fault characteristic frequencies and multiplicative amplitudes,showing its certain application prospects and value in the early diagnosis of rolling bearing faults.
作者
雷虎
王靖岳
郑珺文
侯兴达
丁建明
LEI Hu;WANG Jing-yue;ZHENG Jun-wen;HOU Xing-da;DING Jian-ming(School of Automobile and Transportation,Shenyang Ligong University,Shenyang 110159,China;State Key Laboratory of Traction Power,Southwest Jiaotong University,Chengdu 610031,China)
出处
《失效分析与预防》
2024年第4期264-272,共9页
Failure Analysis and Prevention
基金
辽宁省自然科学基金(2020-MS-216)
辽宁省“百千万人才工程”经费资助项目(2020921031)
牵引动力国家重点实验室开放基金资助项目(TPL2310)。
关键词
特征模态分解
鹈鹕优化算法
早期故障
滚动轴承
故障诊断
feature mode decomposition
pelican optimization algorithm
early fault
rolling bearing
fault diagnosis
