摘要
滚动轴承早期故障特征信息十分微弱并夹杂着环境噪声的干扰,使其信噪比极低,造成微弱故障难以提取。针对这一问题,提出了一种基于自适应局部迭代滤波(Adaptive local iterative filter, ALIF)和最大相关峭度解卷积(Maximum correlated kurtosis deconvolution, MCKD)两者相结合的滚动轴承早期故障诊断方法。首先对采集到的振动信号应用ALIF进行分解得到若干个窄带本征模态函数(Intrinsic mode functions, IMFs),根据相关系数-峭度准则筛选出两个较为敏感的IMF分量进行重构降噪;然后对重构降噪后的信号采用MCKD算法增强故障特征中的冲击成分;最后对应用ALIF-MCKD增强后的信号进行包络谱解调分析,提取出故障特征从而判断轴承故障发生位置。
Early fault feature of rolling bearings is very weak and affected by environmental noise,which makes its signal-to-noise ratio very low and makes it difficult to extract the weak fault feature.To solve this problem,this paper put forward an integrated diagnosis method based on the adaptive local iterative filter(ALIF)and maximum correlated kurtosis deconvolution(MCKD).Firstly the collected vibration signals were decomposed by ALIF into a number of narrow band intrinsic mode functions(IMFs),and two sensitive IMF components screened out according to the correlation coefficient-kurtosis criterion are reconstructed for noise reduction.Then,the MCKD algorithm is used to enhance the shock component of the fault characteristics.Finally,the envelope spectrum demodulation analysis was carried out for the signal enhanced by the application of ALIF-MCKD and the fault characteristics were extracted to determine the bearing fault location.
作者
陈明
马洁
CHEN Ming;MA Jie(School of Automation,Beijing Information Science and Technology University,Beijing 100192,China;School of Mechanical Electrical Engineering,Beijing Information Science and Technology University,Beijing 100192,China)
出处
《机械科学与技术》
CSCD
北大核心
2021年第7期1016-1024,共9页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(61973041)。
关键词
滚动轴承
故障诊断
自适应局部迭代滤波
最大相关峭度解卷积
rolling bearing
fault diagnosis
adaptive local iterative filtering
maximum correlation kurtosis deconvolution
