Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis  

移不变字典学习算法提取冲击成分及其在齿轮箱轴承早期故障诊断中的应用（英文）

作　　者：ZHANG Zhao-heng DING Jian-ming WU Chao LIN Jian-hui 张兆珩;丁建明;吴超;林建辉(State Key Laboratory of Traction Power, Southwest Jiaotong University;CRRC Zhuzhou Locomotive Co., Ltd)

机构地区：[1]State Key Laboratory of Traction Power, Southwest Jiaotong University [2]CRRC Zhuzhou Locomotive Co., Ltd

出　　处：《Journal of Central South University》2019年第4期824-838,共15页中南大学学报（英文版）

基　　金：Project(51875481) supported by the National Natural Science Foundation of China;Project(2682017CX011) supported by the Fundamental Research Foundations for the Central Universities,China;Project(2017M623009) supported by the China Postdoctoral Science Foundation;Project(2017YFB1201004) supported by the National Key Research and Development Plan for Advanced Rail Transit,China;Project(2019TPL_T08) supported by the Research Fund of the State Key Laboratory of Traction Power,China

摘　　要：The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.由轴承故障所产生的故障冲击成分是对齿轮箱轴承故障评估的关键特征。但是由于严重的背景噪声与其它振动的干扰,从测量的振动信号中提取这些故障尤其是早期故障的冲击成分是十分困难的。为了捕捉到这些隐藏在测量的振动信号中的冲击成分的高阶特征,应用一种被称作移不变K均值奇异值分解字典学习算法(SI-K-SVD)对齿轮箱轴承进行早期故障检测。尽管移不变K均值奇异值分解字典学习算法比现有的大部分算法更具有灵活性与自适应性,但与该算法密切相关的两种参数(迭代次数与模式的长度)的不合理选取,会对故障诊断的效果产生负面影响。因此,该算法引入包络谱的稀疏度与峭度值并分别用于选取上述两种参数。基于这两种参数优化选取的移不变K均值奇异值分解字典学习算法,被称为最优参数移不变奇异值分解字典学习算法(OP-SI-K-SVD),本文采用该算法用于齿轮箱轴承的故障检测。通过对仿真与台架试验的数据的分析,验证了该算法的有效性。同时通过与现有的几种先进算法(经验模态分解、小波变换和K均值奇异值分解)的对比,最优参数移不变奇异值分解字典学习算法在齿轮箱轴承的早期故障诊断中展现出了优异的性能。

关 键 词：gear-box bearing fault diagnosis shift-invariant K-means singular value decomposition impulsive component extraction 

分 类 号：TH133.3[机械工程—机械制造及自动化]