基于相关分析和Lempel-Ziv指标的轴承损伤程度识别
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摘要
针对单一故障模式下轴承内、外圈损伤程度的区分问题,提出了一种基于相关分析的Lempel-Ziv指标评估方法。通过相关分析在保留信号中频率成分的基础上来降低信号中噪声对Lempel-Ziv指标的影响。首先,该方法对原始信号进行自相关分析,将信号进行降噪;其次,对降噪处理后的信号进行0-1编码,得到信号的编码序列;最后,对编码后的序列计算Lempel-Ziv指标,得到信号的复杂度。通过仿真和轴承故障实验数据验证了所提方法的有效性。与传统的Lempel-Ziv指标及滤波后的Lempel-Ziv指标相比,在噪声环境下,所提方法能较好地识别噪声中轴承故障信号的复杂程度,能够有效的区分单一故障模式下轴承内、外圈的损伤严重程度。
For the damage degree recognition of bearing inner and outer ring under the single failure mode,an evaluation method based on the Lempel-Ziv index and correlation analysis is proposed.The influence of noise on Lempel-Ziv index is reduced by correlation analysis on the basis of retaining frequency components in the signal.Firstly,the autocorrelation analysis of the original signal is used to reduce the noise component of the signal.Secondly,the coding sequence of the original signal is obtained by 0-1 encoding of the noise reduction signal.Finally the sequence after coding is used to calculate the Lempel-Ziv index to get the signal complexity.The effectiveness of the proposed method is verified by simulation and experimental data.Compared with the traditional Lempel-Ziv complexity and the Lempel-Ziv complexity after filtering,the proposed method can identify the bearing fault signal complexity in the noise environment,which can effectively distinguish the damage degree of the bearing inner and outer ring under the single failure mode.
For the damage degree recognition of bearing inner and outer ring under the single failure mode,an evaluation method based on the Lempel-Ziv index and correlation analysis is proposed.The influence of noise on Lempel-Ziv index is reduced by correlation analysis on the basis of retaining frequency components in the signal.Firstly,the autocorrelation analysis of the original signal is used to reduce the noise component of the signal.Secondly,the coding sequence of the original signal is obtained by 0-1 encoding of the noise reduction signal.Finally the sequence after coding is used to calculate the Lempel-Ziv index to get the signal complexity.The effectiveness of the proposed method is verified by simulation and experimental data.Compared with the traditional Lempel-Ziv complexity and the Lempel-Ziv complexity after filtering,the proposed method can identify the bearing fault signal complexity in the noise environment,which can effectively distinguish the damage degree of the bearing inner and outer ring under the single failure mode.
引文
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[18]张超,陈建军.基于LMD和Lempel-Ziv指标的滚动轴承故障损伤程度研究[J].振动与冲击,2012,31(16):77-82.Zhang Chao,Chen Jianjun.Fault severity assessment for rolling element bearings based on LMD and Lempel-Zivindex[J].Journal of Vibration and Shock,2012,31(16):77-82.(in Chinese)
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[20]王建国,吴林峰,秦绪华.基于自相关分析和LMD的滚动轴承振动信号故障特征提取[J].中国机械工程,2014,25(2):186-191.Wang Jianguo,Wu Linfeng,Qin Xuhua.Rolling bearing vibration signal fault feature extraction based on autocorrelation analysis and LMD[J].China Mechanical Engineering,2014,25(2):186-191.(in Chinese)
[21]何威.机械故障诊断中的微弱信号提取方法的研究[D].北京:北京化工大学,2010.
[22]朱永生,袁幸,张优云,等.滚动轴承复合故障振动建模及Lempel-Ziv复杂度评价[J].振动与冲击,2013,32(16):23-29.Zhu Yongsheng,Yuan Xing,Zhang Youyun,et al.Vibration modeling of rolling bearings considering compound multi-defects and appraisal with LempelZivcomplexity[J].Journal of Vibration and Shock,2013,32(16):23-29.(in Chinese)
[23]Kuo S M,Morgan D R.Active noise control:a tutorial review[J].Proceedings of the IEEE,1999,87(6):943-973.
[24]西储大学故障轴承公开数据[DB/OL].(2017-04-20)[2017-05-20].http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
[2]Tyagi S.A Comparative study of SVM classifiers and artificial neural networks application for rolling element bearing fault diagnosis using wavelet transform preprocessing[J].Proceedings of World Academy of Science Engineering and Technolog,2008,45:309-317.
[3]Jantunen E.How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods[J].International Journal of Comadem,2006,9(3):24-38.
[4]Dyer D,Stewart R M.Detection of rolling element bearing damage by statistical vibration analysis[J].Journal of Mechanical Design,1978,100(2):115.
[5]Mcfadden P D,Smith J D.Vibration monitoring of rolling element bearings by the high-frequency resonance technique-a review[J].Tribology International,1984,17(84):3-10.
[6]Kankar P K,Sharma S C,Harsha S P.Rolling element bearing fault diagnosis using wavelet transform[J].Neurocomputing,2011,74(10):1638-1645.
[7]Yu Yang,Yu Dejie,Cheng Junsheng.A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J].Journal of Sound and Vibration,2006,294(1):269-277.
[8]Li Yongbo,Xu Minqiang,Wei Yu,et al.An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis[J].Measurement,2015,63:330-345.
[9]Lempel A,Ziv J.On the complexity of finite sequences[J].IEEE Transactions on Information Theory,1976,22(1):75-81.
[10]Mateo A,Roberto H,Daniel A,et al.Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis[J].IEEE Transactions on Biomedical Engineering,2006,53(11):2282-2288.
[11]Hu Jing,Gao Jianbo,Principe J C.Analysis of biomedical signals by the Lempel-Ziv complexity:the effect of finite data size[J].IEEE Transactions on Biomedical Engineering,2006,53(12):2606-2609.
[12]Nagarajan R.Quantifying physiological data with Lempel-Zivcomplexity-certain issues[J].IEEE Transactions on Biomedical Engineering,2002,49(11):1371-1373.
[13]Zhang Xusheng,Roy R J,Jensen E W.EEG complexity as a measure of depth of anesthesia for patients[J].IEEE Transactions on Biomedical Engineering,2001,48(12):1424-1433.
[14]Yan Ruqiang,Gao Robert X.Complexity as a measure for machine health evaluation[J].IEEE Transactions on Instrumentation and Measurement,2004,53(4):1327-1334.
[15]Hong Hoonbin,Liang Ming.Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform[J].Journal of Sound and Vibration,2009,320(1):452-468.
[16]窦东阳,赵英凯.基于EMD和Lempel-Ziv指标的滚动轴承损伤程度识别研究[J].振动与冲击,2010,29(3):5-8.Dou Dongyang,Zhao Yingkai.Fault severity assessment for rolling element bearings based on EMD and Lempel-Ziv index[J].Journal of Vibration and Shock,2010,29(3):5-8.(in Chinese)
[17]何雷,谭建平,尹芳莉,等.基于LMD和Lempel-Ziv指标的轴承径向磨损程度识别[J].机械传动,2014,38(8):34-38.He Lei,Tan Jianping,Yin Fangli,et al.Radial wear degree recognition of bearing based on LMD and Lempel-Zivindex[J].Journal of Mechanical Transmission,2014,38(8):34-38.(in Chinese)
[18]张超,陈建军.基于LMD和Lempel-Ziv指标的滚动轴承故障损伤程度研究[J].振动与冲击,2012,31(16):77-82.Zhang Chao,Chen Jianjun.Fault severity assessment for rolling element bearings based on LMD and Lempel-Zivindex[J].Journal of Vibration and Shock,2012,31(16):77-82.(in Chinese)
[19]Lei Yaguo,He Zhengjia,Zi Yanyang.A new approach to intelligent fault diagnosis of rotating machinery[J].Expert Systems with Applications,2008,35(4):1593-1600.
[20]王建国,吴林峰,秦绪华.基于自相关分析和LMD的滚动轴承振动信号故障特征提取[J].中国机械工程,2014,25(2):186-191.Wang Jianguo,Wu Linfeng,Qin Xuhua.Rolling bearing vibration signal fault feature extraction based on autocorrelation analysis and LMD[J].China Mechanical Engineering,2014,25(2):186-191.(in Chinese)
[21]何威.机械故障诊断中的微弱信号提取方法的研究[D].北京:北京化工大学,2010.
[22]朱永生,袁幸,张优云,等.滚动轴承复合故障振动建模及Lempel-Ziv复杂度评价[J].振动与冲击,2013,32(16):23-29.Zhu Yongsheng,Yuan Xing,Zhang Youyun,et al.Vibration modeling of rolling bearings considering compound multi-defects and appraisal with LempelZivcomplexity[J].Journal of Vibration and Shock,2013,32(16):23-29.(in Chinese)
[23]Kuo S M,Morgan D R.Active noise control:a tutorial review[J].Proceedings of the IEEE,1999,87(6):943-973.
[24]西储大学故障轴承公开数据[DB/OL].(2017-04-20)[2017-05-20].http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
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