摘要
针对特征模态分解(FMD)在处理复合故障时参数难以选取的问题,提出了一种基于鲸鱼优化算法(WOA)优化FMD的滚动轴承复合故障诊断方法。首先,基于信号频谱能量和模态分布,设计了一个综合评价指标——自适应加权频域峰度与交叉信息熵的比值,并将其作为目标函数,该指标不仅能够精准捕捉信号的故障特征,还能在分解过程中平衡各模态之间的关系;然后,利用WOA对FMD中的两个关键参数(即模态数n和滤波器长度L)进行了自适应优化,以调整到最佳值,确保FMD分解结果既能充分提取故障特征,又能有效抑制噪声干扰;最后,基于内蒙古科技大学机械工程学院的HZXT-DS-003双跨转子滚动轴承试验平台,构建了涵盖多种复合故障模式的轴承数据集,并进行了实验分析。仿真与实验研究结果表明:该方法在噪声抑制方面表现出色,能够有效识别复合故障中相对较弱的故障特征频率,从而显著提升了滚动轴承复合故障诊断的准确性和可靠性;此外,通过将该方法与对比方法进行了多方面的定性和定量对比分析,进一步验证了该方法的优越性。可见基于WOA优化FMD的故障诊断方法可以对滚动轴承复合故障进行有效诊断。
Aiming at the problem that the parameters of the feature mode decomposition(FMD)were difficult to select when dealing with composite faults,a diagnostic method based on the whale optimization algorithm(WOA)to optimize the FMD for composite faults was proposed.First,based on the signal spectral energy and modal distribution,a comprehensive evaluation index,the ratio of adaptive weighted frequency domain kurtosis to cross-information entropy,was designed and used as the objective function,which not only accurately captured the fault characteristics of the signal,but also balanced the relationship between modes during the decomposition process.Then,the WOA was utilized to optimize the two key parameters in the FMD,i.e.,the number of modes n and the filter length L,were adaptively optimized and adjusted to the optimal values to ensure that the FMD decomposition results not only fully extract the fault characteristics,but also effectively suppress the noise interference.Finally,based on the HZXT-DS-003 double-span rotor rolling bearing test platform of the School of Mechanical Engineering of Inner Mongolia University of Science and Technology,a bearing dataset covering multiple composite fault modes was constructed and experimentally analyzed.The results of the simulation and experimental studies show that the proposed method performs well in noise suppression and is able to effectively identify the relatively weak fault eigenfrequencies in the composite faults,thus significantly improving the accuracy and reliability of the composite fault diagnosis of rolling bearings.In addition,the method's superiority is further verified through qualitative and quantitative comparative analyses with the comparison method.It can be seen that the fault diagnosis method based on WOA optimized FMD can effectively diagnose the composite faults of rolling bearings.
作者
徐帅
张超
XU Shuai;ZHANG Chao(School of Mechanical Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China;Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region,Baotou 014010,China)
出处
《机电工程》
北大核心
2025年第8期1440-1449,共10页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(52365014)
中央引导地方科技发展资金资助项目(2022ZY0221)
内蒙古自治区重点研发和成果转化计划项目(2023YFSW0003)
内蒙古自治区直属高校基本科研业务费项目(2024YXXS045)。
关键词
滚动轴承故障诊断
特征模态分解
鲸鱼优化算法
自适应加权频域峰度与交叉信息熵比值
故障特征提取
噪声干扰抑制
rolling bearing fault diagnosis
feature mode decomposition(FMD)
whale optimization algorithm(WOA)
ratio of self-adaptive weighted frequency domain kurtosis to cross information entropy
fault feature extraction
noise interference suppression
