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自适应最大相关峭度解卷积方法及其在轴承早期故障诊断中的应用

DOI: 10.13334/j.0258-8013.pcsee.2015.06.019, PP. 1436-1444

Keywords: 滚动轴承,早期故障,参数优化,自适应解卷积,相关峭度

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Abstract:

滚动轴承处于早期故障阶段时,特征信号微弱,并且受环境噪声影响严重,因此故障特征提取困难。针对这一问题,提出了基于自适应最大相关峭度解卷积的滚动轴承早期故障诊断方法。利用粒子群算法优良的寻优特性,并行搜寻最大相关峭度解卷积算法的影响参数,自适应地实现最佳的解卷积效果。故障信号通过影响参数优化的最大相关峭度解卷积算法处理后,冲击特性会得到增强,对解卷积信号做进一步包络解调分析,通过分析包络谱中幅值突出的频率成分可最终判定故障类型。仿真和实测信号分析结果表明,该方法可有效提取滚动轴承早期故障微弱特征频率信息。

References

[1]  刘中磊,于德介,刘坚.基于故障特征频率的阶比双谱方法及其在滚动轴承故障诊断中的应用[J].中国电机工程学报,2013,33(33):123-129.Liu Zhonglei,Yu Dejie,Liu Jian.Order bispectrum analysis based on fault characteristic frequency and its application to the fault diagnosis of rolling bearings[J].Proceedings of the CSEE,2013,33(33):123-129(in Chinese).
[2]  胥永刚,孟志鹏,陆明,等.双树复小波和奇异差分谱在滚动轴承故障诊断中的应用[J].振动工程学报,2013,26(6):965-973.Xu Yonggang,Meng Zhipeng,Lu Ming,et al.Application of dual tree complex wavelet transform and singular value difference spectrum in the rolling bearing fault diagnosis[J].Journal of Vibration Engineering,2013,26(6):965-973(in Chinese).
[3]  胡爱军,马万里,唐贵基.基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法[J].中国电机工程学报,2012,32(11):106-111.Hu Aijun,Ma Wanli,Tang Guiji.Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and kurtosis criterion[J].Proceedings of the CSEE,2012,32(11):106-111(in Chinese).
[4]  雷亚国,韩冬,林京,等.自适应随机共振新方法及其在故障诊断过中的应用[J].机械工程学报,2012,48(7):62-67.Lei Yaguo,Han Dong,Lin Jing,et al.New adaptive stochastic resonance method and its application to fault diagnosis[J].Journal of Mechanical Engineering,2012,48(7):62-67(in Chinese).
[5]  罗忠辉,薛晓宁,王筱珍,等.小波变换及经验模式分解方法在电机轴承早期故障诊断中的应用[J].中国电机工程学报,2005,25(14):125-129.Luo Zhonghui,Xue Xiaoning,Wang Xiaozhen,et al.Study on the method of incipient motor bearing fault diagnosis based on wavelet transform and EMD[J].Proceedings of the CSEE,2005,25(14):125-129(in Chinese).
[6]  曾庆虎,邱静,刘冠军,等.基于小波相关滤波一包络分析的早期故障特征提取方法[J].仪器仪表学报,2008,29(4):729-933.Zeng Qinghu,Qiu Jing,Liu Guanjun,et al.Approach to extraction of incipient fault features based on wavelet correlation filter and envelope analysis[J].Chinese Journal of Scientific Instrument,2008,29(4):729-933(in Chinese).
[7]  Ming Y,Chen J,Dong G M.Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum[J].Mechanical System and Signal Processing,2011,25(5):1773-1785.
[8]  莫代一,崔玲丽,王婧.基于双重 Q因子的稀疏分解法在滚动轴承早期故障诊断中的应用[J].机械工程学报,2013,49(9):37-41.Mo Daiyi,Cui Lingli,Wang Jing.Sparse signal decomposition method based on the dual Q-factor and its application to rolling bearing early fault diagnosis[J].Journal of Mechanical Engineering,2013,49(9):37-41(in Chinese).
[9]  Jiang R L,Chen J,Dong G M,et al.The Weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum[J].Engineering Science Engineers,Part C:Journal of Mechanical Engineering Science,2013,227(5):1116-1129.
[10]  苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.Su Wensheng,Wang Fengtao,Zhang Zhixin,et al.Application of EMD denoising and spectral kurtosis in early diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21(in Chinese).
[11]  Mcdonald G L,Zhao Q,Zuo M J.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.
[12]  Kennedy J,Eberhart R C.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,Perth,Australia,1995:1942-1948.
[13]  沈伋,韩丽川,沈益斌.基于粒子群算法的飞机总体参数优化[J].航空学报,2008,29(6):1538-1541.Shen Ji,Han Lichuan,Shen Yibin.Optimization of airplane primary parameters based on particle swarm algorithm[J].Acta Aeronautica et Astronautica Sinica,2008,29(6):1538-1541(in Chinese).
[14]  李宁.粒子群优化算法的理论分析与应用研究[D].武汉:华中科技大学,2006.Li Ning.Analysis and application of particle swarm optimization[D].Wuhan:Huazhong University of Science and Technology,2006(in Chinese).
[15]  Su W S,Wang F T,Zhu H,et al.Rolling element bearing faults diagnosis based on optimal morlet wavelet filter and autocorrelation enhancement[J].Mechanical System and Signal Processing,2010,24(5):1458-1472.
[16]  Antoni J,Bonnardot F,Raad A,et al.Cyclostationary modeling of rotating machine vibration signals[J].Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
[17]  王宏超,陈进,董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J].机械工程学报,2013,49(1):88-94.Wang Hongchao,Chen Jin,Dong Guangming.Fault diagnosis method for rolling bearing’s weak fault based on minimum entropy deconvolution and sparse decomposition[J].Journal of Mechanical Engineering,2013,49(1):88-94(in Chinese).
[18]  Randall R B,Antoni J,Chobsaard S.The relationship between spectral correlation and envelope analysis in the diagnosis of bearing faults and other cyclostationary machine signals[J].Mechanical Systems and Signal Processing,2001,15(5):945-962.
[19]  马伦,康建设,孟妍,等.基于Morlet小波变换的滚动轴承早期故障特征提取研究[J].仪器仪表学报,2013,34(4):920-926.Ma Lun,Kang Jianshe,Meng Yan,et al.Research on feature extraction of rolling bearing incipient fault based on morlet wavelet transform[J].Chinese Journal of Scientific Instrument,2013,34(4):920-926(in Chinese).
[20]  Qiu H,Lee J,Lin J,et al.Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J].Journal of Sound and Vibration,2006,289(4-5):1066-1090.

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