摘要
为了解决实际工况中故障信号被噪声掩盖,故障特征频率难以提取的问题,提出改进最大峭度解卷积(MCKD)和改进的集总平均经验模态分解(MEEMD)结合的滚动轴承故障诊断方法。首先,提出使用合成峭度作为指标来选取MCKD的最优参数:位移数M和最大滤波器长度L;然后将最优参数代入MCKD算法中,得到最佳降噪信号;最后对降噪信号使用MEEMD分解,得到若干本征模态分量(IMF),选取合适的分量做信号重构,再对重构信号做频谱分析,在频谱中可以寻找出故障频率以及其他的信息。通过仿真分析了MEEMD方法的优越性及不足之处,并使用改进MCKD方法对不足处进行了改进,将改进MCKD-MEEMD方法与MEEMD方法以及传统MCKD-MEEMD方法进行了实验对比分析,证明了改进MCKD-MEEMD方法的故障诊断效果更好。
In order to solve the problem that the fault signal is covered by noise in actual working conditions and the fault characteristic frequency is difficult to extract,a rolling bearing fault diagnosis method combining improved maximum kurtosis deconvolution(MCKD)and improved lumped average empirical mode decomposition(MEEMD)is proposed.First,it is proposed to use synthetic kurtosis as an index to select the optimal parameters of MCKD:the number of displacements M and the maximum filter length L;then the optimal parameters are substituted into the MCKD algorithm to obtain the best noise reduction signal;finally,the noise reduction signal is used MEEMD decomposes to obtain a number of intrinsic modal components(IMF),selects appropriate components for signal reconstruction,and then performs spectrum analysis on the reconstructed signal.In the spectrum,the fault frequency and other information can be found.The advantages and disadvantages of the MEEMD method are analyzed through simulation,and the deficiencies are improved by using the improved MCKD method.The improved MCKD-MEEMD method is compared with the MEEMD method and the traditional MCKD-MEEMD method,and the improvement is proved.The fault diagnosis effect of MCKD-MEEMD method is better.
作者
张超
秦敏敏
张少飞
ZHANG Chao;QIN Min-min;ZHANG Shao-fei(School of Mechanical Engineering,Inner Mongolia University of Science and Technology,Inner Mongolia Baotou 014010,China;Key Laboratory of Intelligent Diagnosis and Control for Electromechanical System of Inner Mongolia Autonomous Region,Inner Mongolia Baotou 014010,China)
出处
《机械设计与制造》
北大核心
2024年第7期193-199,共7页
Machinery Design & Manufacture
基金
国家自然科学基金(51965052)。
关键词
最大相关峭度解卷积
合成峭度
经验模态分解
故障诊断
Maximum Correlation Kurtosis Deconvolution
Synthetic Kurtosis
Empirical Mode Decomposition
Fault Diagnosis
