基于自适应最优Morlet小波的滚动轴承故障诊断
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摘要
滚动轴承早期故障信号中故障信息比较微弱常常被强噪声所掩盖,增加了对滚动轴承故障诊断的难度。针对这一问题,笔者提出了基于自适应最优Morlet小波变换的滚动轴承故障诊断方法。首先,利用粒子群优化算法对Morlet小波变换的核心参数进行自适应寻优,在获得最优Morlet小波的同时保证了良好的带通滤波性能;然后,将最优Morlet小波对滚动轴承早期故障信号进行滤波去噪,提高信号的信噪比;最后,对最优Morlet小波滤波信号进行包络谱分析,通过包络谱中的主导频率成分与滚动轴承各元件的故障特征频率对比从而判断轴承的故障位置。仿真数据和实测数据分析结果证明,笔者所提方法能够有效提取故障信号中的特征信息,具有一定的有效性。
The early stage weak impulsive fault feature is so weak that it is always covered by environmental noise,which increases the fault diagnosis difficulty of rolling bearing.Aiming to this problem,a new diagnosis method based on adaptive optimal Morlet wavelet transform is proposed.Firstly,The core parameter of Morlet wavelet transform is calculated by particle swarm optimization(PSO)adaptively,which guarantees optimal Morlet based wavelet as well as wonderful band-pass filter performance;Secondly,in order to improve signal-to-noise ratio,optimal Morlet wavelet is used to filter incipient fault signal of rolling bearing;Finally,optimal Morlet wavelet filtered signal is analyzed by envelope spectrum,and the fault location of rolling bearing is extracted by contrasting the major frequency with the fault frequency of rolling bearing.The analysis results of simulated signal and measured signal show that the proposed method is able to extract the fault impulse signal.
The early stage weak impulsive fault feature is so weak that it is always covered by environmental noise,which increases the fault diagnosis difficulty of rolling bearing.Aiming to this problem,a new diagnosis method based on adaptive optimal Morlet wavelet transform is proposed.Firstly,The core parameter of Morlet wavelet transform is calculated by particle swarm optimization(PSO)adaptively,which guarantees optimal Morlet based wavelet as well as wonderful band-pass filter performance;Secondly,in order to improve signal-to-noise ratio,optimal Morlet wavelet is used to filter incipient fault signal of rolling bearing;Finally,optimal Morlet wavelet filtered signal is analyzed by envelope spectrum,and the fault location of rolling bearing is extracted by contrasting the major frequency with the fault frequency of rolling bearing.The analysis results of simulated signal and measured signal show that the proposed method is able to extract the fault impulse signal.
引文
[1]胥永刚,孟志鹏,陆明.基于双树复小波包变换的滚动轴承故障诊断[J].农业工程学报,2013,29(10):49-56.Xu Yonggang,Meng Zhipeng,Lu Ming.Fault diagnosis of rolling bearing based on dual-tree complex wavelet packet transform[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(10):49-56.(in Chinese)
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[10]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.
[11]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)
[12]Qiu Hai,Lee Jay,Lin Jing,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.
[2]梁霖,徐光华,侯成刚.基于奇异值分解的连续小波消噪方法[J].西安交通大学学报,2004,39(9):904-908.Liang Lin,Xu Guanghua,Hou Chenggang.Continuous wavelet transform denoising method based on singular value decomposition[J].Journal of Xi'an Jiaotong University,2004,39(9):904-908.(in Chinese)
[3]Lin Jing,Qu Liangsheng.Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis[J].Journal of Sound&Vibration,2000,234(1):135-148.
[4]蒋永华,汤宝平,董绍江.自适应Morlet小波降噪方法及在轴承故障特征提取中的应用[J].仪器仪表学报,2010,31(12):2712-2717.Jiang Yonghua,Tang Baoping,Dong Shaojiang.Denoising method based on adaptive Morlet wavelet and its application in rolling bearing fault feature extraction[J].Chinese Journal of Scientific Instrument,2010,31(12):2712-2717.(in Chinese)
[5]耿宇斌.基于Morlet小波与SVD的旋转机械故障特征提取算法研究[D].广州:华南理工大学,2015.
[6]张丹,隋文涛,张宇.基于最优Morlet小波的滚动轴承故障诊断[J].轴承,2009(10):48-51.Zhang Dan,Sui Wentao,Zhang Yu.Bearing fault diagnosis based on optimal morlet wavelet[J].Bering,2009(10):48-51.(in Chinese)
[7]侯新国,牛超,杨忠林.基于最优Morlet小波自适应包络解调的弱故障特征提取方法[J].电机与控制学报,2016,20(10):88-93.Hou Xinguo,Niu Chao,Yang Zhonglin.Method to extract weak fault feature based on optimal Morlet wavelet adaptive envelope demodulation[J].Electric Machines and Control,2016,20(10):88-93.(in Chinese)
[8]Antoni J,Bnnardot F,Raad A,et al.Cyclostationary modeling of rotating machine vibration signals[J].Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
[9]唐贵基,王晓龙.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[J].中国电机工程学报,2015,35(6):1436-1444.Tang Guiji,Wang Xiaolong.Adaptive maximum correlated kurtosis deconvolution method and its application on incipient fault diagnosis of bearing[J].Proceedings of the Chinese Society for Electrical Engineering,2015,35(6):1436-1444.(in Chinese)
[10]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.
[11]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)
[12]Qiu Hai,Lee Jay,Lin Jing,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.