基于级联过完备字典稀疏表征的滚动轴承复合故障诊断方法

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摘要针对滚动轴承多故障特征相互耦合不易诊断的问题，提出了一种将级联过完备字典与以基追踪降噪问题为优化原则的特征符号搜索算法（FSS）相结合的轴承复合故障诊断方法。该方法根据轴承故障冲击响应信号的特点，以轴承阻尼二阶系统的单位冲击响应函数作为级联过完备字典基函数，并通过相关滤波法获取构建字典的参数。结合得到的字典，使用特征符号搜索算法对信号中不同类型的故障冲击成分进行稀疏分解并重构，进而实现各故障特征的分离和提取。轴承复合故障仿真信号和实测信号的分析结果表明，所提方法能有效地分离和提取出复合故障中的各类故障特征，同时对降噪具有极好的鲁棒性。

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	郑胜
	刘韬
	刘畅
	李华

关键词 ：轴承复合故障, 级联过完备字典, 基追踪降噪, 特征符号搜索(FSS), 相关滤波

Abstract：Aiming at the problem that multi fault features of rolling bearing are coupled and difficult to diagnose, a bearing compound faults diagnosis method is proposed, which combines cascaded over complete dictionary with feature sign search algorithm (FSS) based on the optimization principle of basis pursuit de-noising.According to the characteristics of the bearing fault impulse response signal, the unit impulse response function of the bearing damping second-order system is used as the basis function of the cascaded over complete dictionary, and the parameters of the dictionary are obtained by the correlation filtering method.Combining the obtained dictionary, using the feature sign search algorithm to sparsely decompose and reconstruct different types of fault impact components in the signal, and then realize the separation and extraction of each fault feature.The analysis results of the simulated and measured signals of bearing compound faults show that the proposed method can effectively separate and extract various fault features in compound faults, and at the same time has excellent robustness to noise reduction.

Key words： bearing compound faults cascaded over complete dictionary basis pursuit de-noising feature sign search(FSS);correlation filtering

收稿日期: 2020-02-25 出版日期: 2021-05-28

引用本文:

郑胜，刘韬，刘畅，李华. 基于级联过完备字典稀疏表征的滚动轴承复合故障诊断方法[J]. 振动与冲击, 2021, 40(10): 174-179.
ZHENG Sheng,LIU Tao,LIU Chang,LI Hua. Compound faults diagnosis method of rolling bearing based on sparse representation of cascaded over complete dictionary. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(10): 174-179.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I10/174

［1］张菀.基于信号稀疏表示的滚动轴承微弱故障特征提取方法研究［D］.南京：东南大学，2018.

［2］OLSHAUSEN B A.Emergence of simple-cell receptive field properties by learning a sparse code for natural images ［J］.Nature, 1996, 381(6583): 607-609.

［3］ABROL V, SHARMA P, SAO A K.Greedy double sparse dictionary learning for sparse representation of speech signals［J］.Speech Communication, 2016, 85:71-82.

［4］ELAD M, AHARON M.Image denoising via sparse and redundant representations over learned dictionaries ［J］.Image Processing, IEEE Transactions on, 2006, 15(12): 3736-3745.

［5］YANG J, WRIGHT J, HUANG T S, et al.Image super-resolution via sparse representation ［J］.Image Processing, IEEE Transactions on, 2010, 19(11): 2861-2873.

［6］杨琴.基于压缩感知的旋转机械故障分类与识别方法研究［D］.北京：北京化工大学，2016.

［7］DUARTE M F, DAVENPORT M A, TAKHAR D, et al.Single-pixel imaging via compressive sampling ［J］.Signal Processing Magazine IEEE, 2008, 25(2): 83-91.

［8］李景乐.基于字典学习的稀疏表示故障诊断方法研究［D］.北京: 北京化工大学,2018.

［9］DU Z H, CHEN X F, ZHANG H, et al.Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis［J］.IEEE Transactions on Industrial Electronics, 2015, 62(10): 6594-6605.

［10］唐承志.基于级联原子库稀疏分解及其应用［D］.成都：西南交通大学，2011.

［11］陈向民，于德介，罗洁思.基于信号共振稀疏分解的包络解调方法及其在轴承故障诊断中的应用［J］.振动工程学报，2012,25(6): 628-636.
CHEN Xiangmin, YU Dejie, LUO Jiesi.Envelope demodulation method based on signal resonance sparse decomposition and its application in bearing fault diagnosis ［J］.Journal of Vibration Engineering, 2012,25(6): 628-636.

［12］余发军，周凤星，严保康.基于字典学习的轴承早期故障稀疏特征提取［J］.振动与冲击，2016,35(6): 181-186.
YU Fajun, ZHOU Fengxing, YAN Baokang.Sparse feature extraction of bearing early fault based on dictionary learning ［J］.Journal of Vibration and Shock, 2016,35(6): 181-186.

［13］王林,蔡改改,高冠琪,等.基于改进MP的稀疏表示快速算法及其滚动轴承故障特征提取应用［J］.振动与冲击,2017,36(3):176-182.
WANG Lin, CAI Gaigai, GAO Guanqi, et al.Fast sparse representation algorithm based on improved MP and its application in fault feature extraction of rolling bearing ［J］.Journal of Vibration and Shock, 2017,36(3): 176-182.

［14］LI J, WANG H, WANG X, et al.Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising［J］.Measurement, 2020, 152: 14.

［15］HE G L, DING K B, LIN H B.Fault feature extraction of rolling element bearings using sparse representation［J］.Journal of Sound and Vibration, 2016, 366: 514-527.

［16］TANG H F, CHEN J, DONG G M.Sparse representation based latent components analysis for machinery weak fault detection［J］.Mechanical Systems & Signal Processing, 2014,46(2):373-388.

［17］唐海峰.基于信号稀疏表征的故障诊断方法研究［D］.上海:上海交通大学,2014.

［18］CHEN S, DONOHO D L, SAUNDERS M A.Atomic decomposition by basis pursuit［J］.Siam Journal on Scientific Computing, 1998, 20(1): 33-61.

［19］DONOHO D L, ELAD M, TEMLYAKOV V N.Stable recovery of sparse overcomplete representations in the presence of noise［J］.IEEE Transactions on Information Theory, 2005,52(1): 6-18.

［20］CHANG S G, YU B, VETTERLI M.Adaptive wavelet thresholding for image denoising and compression［J］.IEEE Transactions on Image Processing, 2000,9(9):1532-1546.

［21］LEE H, BATTLE A, RAINA R, et al.Efficient sparse coding algorithms［C］∥/Advances in Neural Information Processing Systems 19.Cambridge：NIPS,2007.

［22］訾艳阳，李庆祥，何正嘉.Laplace小波相关滤波法与冲击响应提取［J］.振动工程学报，2003(1): 71-74.
ZI Yanyang, LI Qingxiang, HE Zhengjia.Laplace wavelet correlation filtering and shock response extraction ［J］.Journal of Vibration Engineering, 2003 (1): 71-74.

［23］MCFADDEN P D, SMITH J D.Model for the vibration produced by a single point defect in a rolling element bearing［J］.Journal of Sound and Vibration, 1984,96(1): 69-82.

［24］MCFADDEN P D, SMITH J D.The vibration produced by multiple point defects in a rolling element bearing［J］.Journal of Sound and Vibration, 1985,98(2):263-273.