基于参数优化时变滤波经验模态分解的转子故障诊断
|
摘要
针对应用时变滤波经验模态分解(TVFEMD)诊断转子故障时需人为指定带宽阈值和B样条阶数两个参数,存在较大主观性和盲目性的不足,提出一种基于参数优化TVFEMD和希尔伯特变换(HT)诊断转子故障的方法。采用粒子群算法搜索最佳参数组合;并使用最优参数组合进行TVFEMD,得到一系列的本征模态函数(IMF);最后,对IMF进行HT,得到信号的希尔伯特时频图和边际谱,从而诊断出转子的故障类型;分别应用该方法诊断恒定转速的转子不平衡、变转速的油膜涡动两种典型转子故障。结果表明:基于参数优化时变经验模态分解和希尔伯特变换的方法不仅能够实现参数的自动选择,获得良好的分解效果,且能准确识别转子的不平衡、油膜涡动等典型故障;与原始经验模态分解和现有方法相比,具有明显的优越性。
In order to overcome the subjectivity and blindness of using the time-varying filter empirical mode decomposition(TVFEMD) to diagnose rotor faults, a method based on parameter optimization TVFEMD and Hilbert transform(HT) was proposed. First, the particle swarm optimization(PSO) was used to search for the best combination of parameters. Then, the TVFEMD was used to obtain a series of intrinsic mode functions(IMF). Finally, the HT was applied to the IMF to obtain the Hilbert time-frequency diagram and marginal spectrum of the signal, so as to diagnose the rotor fault type. The method has been applied to diagnose two typical rotor faults, i. e. unbalanced rotor with constant speed and oil film whirl with variable speed. The results show that the method based on the parameter optimized time-varying empirical mode decomposition and Hilbert transform can not only automatically select the parameters and achieve good decomposition results, but also accurately identify the typical faults such as rotor imbalance and oil film whirl. Compared with the modal decomposition method, it has obvious advantages.
In order to overcome the subjectivity and blindness of using the time-varying filter empirical mode decomposition(TVFEMD) to diagnose rotor faults, a method based on parameter optimization TVFEMD and Hilbert transform(HT) was proposed. First, the particle swarm optimization(PSO) was used to search for the best combination of parameters. Then, the TVFEMD was used to obtain a series of intrinsic mode functions(IMF). Finally, the HT was applied to the IMF to obtain the Hilbert time-frequency diagram and marginal spectrum of the signal, so as to diagnose the rotor fault type. The method has been applied to diagnose two typical rotor faults, i. e. unbalanced rotor with constant speed and oil film whirl with variable speed. The results show that the method based on the parameter optimized time-varying empirical mode decomposition and Hilbert transform can not only automatically select the parameters and achieve good decomposition results, but also accurately identify the typical faults such as rotor imbalance and oil film whirl. Compared with the modal decomposition method, it has obvious advantages.
引文
[ 1 ] 王广斌,杜晓阳,罗军.面向转子故障特征提取的多尺度拉普拉斯特征映射方法[J].中国机械工程,2016,27(20):2791-2797.WANG Guangbin,DU Xiaoyang,LUO Jun.Multi-scale Laplasse feature mapping method for rotor fault feature extraction [J].China Mechanical Engineering,2016,27(20):2791-2797.
[ 2 ] 郑近德,潘海洋,程军圣.均值优化经验模态分解及其在转子故障诊断中的应用[J].机械工程学报,2018,54(23):93-101.ZHENG Jinde,PAN Haiyang,CHENG Junsheng.Mean value optimization empirical mode decomposition and its application in rotor fault diagnosis [J].Mechanical Engineering Journal,2018,54(23):93-101.
[ 3 ] 程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006,26(1):24-27.CHENG Junsheng,YU Dejie,YANG Yu.Application of EMD method in rotor local rub impact fault diagnosis [J].Vibration,Testing and Diagnosis,2006,26 (1):24-27.
[ 4 ] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings Mathematical Physical & Engineering Sciences,1998,454(1971):903-995.
[ 5 ] ZHENG J,CHENG J,YANG Y.Partly ensemble empirical mode decomposition:an improved noise-assisted method for eliminating mode mixing[J].Signal Processing,2014,96:362-374.
[ 6 ] REHMAN N U,MANDIC D P.Filter bank property of multivariate empirical mode decomposition[J].IEEE Transactions on Signal Processing,2011,59(5):2421-2426.
[ 7 ] DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[ 8 ] GILLES J.Empirical wavelet transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[ 9 ] LI H,LI Z,MO W.A time varying filter approach for empirical mode decomposition[J].Signal Processing,2017,138:146-158.
[10] 林近山,窦春红,寇兴磊.基于时变滤波经验模式分解的齿轮箱故障诊断[J].机械传动,2018(1):98-101.LIN Jinshan,DOU Chunhong,KOU Xinglei.Gearbox fault diagnosis based on time-varying filtering empirical mode decomposition [J].Mechanical Transmission,2018 (1):98-101.
[11] ZHANG X,LIU Z,MIAO Q,et al.An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis[J].Journal of Sound Vibration,2018,418:55-78.
[12] 石春香,李胡生.基于Hilbert边际谱与随机-模糊统计原理的梁桥损伤识别试验研究[J].振动与冲击,2011,30(8):123-127.SHI Chunxiang,LI Husheng.Experimental study on damage identification of beam bridge based on Hilbert marginal spectrum and random fuzzy statistics theory [J].Journal of Vibration and Shock,2011,30 (8):123-127.
[13] 祁树锋,李夕海,韩绍卿,等.基于Hilbert谱区域能量比的核爆与雷电电磁脉冲识别[J].振动与冲击,2013,32(3):163-166.QI Shufeng,LI Xihai,HAN Shaoqing,et al.Discrimination of nuclear explosion and lightning electromagnetic pulse using regional energy ratio of hilbert spectrum [J].Journal of Vibration and Shock,2013,32 (3):163-166.
[14] 邓飞跃,唐贵基,王晓龙.谐波分解结合自互补 Top-Hat变换的轴承微弱故障特征提取方法[J].振动工程学报,2015,28(6):981-989.DENG Feiyue,TANG Guiji,WANG Xiaolong.Harmonic decomposition combined with self complementary Top-Ha transform method of bearing weak fault feature extraction [J].Journal of Vibration Engineering,2015,28 (6):981-989.
[15] 胡爱军,南冰.基于自适应概率主成分分析的滚动轴承故障特征增强方法[J].振动与冲击,2017,36(19):145-150.HU Aijun,NAN Bing.Fault feature enhancement method of rolling bearing based on adaptive probabilistic principal component analysis [J].Journal of Vibration and Shock,2017,36 (19):145-150.
[16] 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 aircraft overall parameters based on particle swarm optimization[J].Journal of Aeronautics,2008,29 (6):1538-1541.
[ 2 ] 郑近德,潘海洋,程军圣.均值优化经验模态分解及其在转子故障诊断中的应用[J].机械工程学报,2018,54(23):93-101.ZHENG Jinde,PAN Haiyang,CHENG Junsheng.Mean value optimization empirical mode decomposition and its application in rotor fault diagnosis [J].Mechanical Engineering Journal,2018,54(23):93-101.
[ 3 ] 程军圣,于德介,杨宇.EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006,26(1):24-27.CHENG Junsheng,YU Dejie,YANG Yu.Application of EMD method in rotor local rub impact fault diagnosis [J].Vibration,Testing and Diagnosis,2006,26 (1):24-27.
[ 4 ] HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings Mathematical Physical & Engineering Sciences,1998,454(1971):903-995.
[ 5 ] ZHENG J,CHENG J,YANG Y.Partly ensemble empirical mode decomposition:an improved noise-assisted method for eliminating mode mixing[J].Signal Processing,2014,96:362-374.
[ 6 ] REHMAN N U,MANDIC D P.Filter bank property of multivariate empirical mode decomposition[J].IEEE Transactions on Signal Processing,2011,59(5):2421-2426.
[ 7 ] DRAGOMIRETSKIY K,ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[ 8 ] GILLES J.Empirical wavelet transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[ 9 ] LI H,LI Z,MO W.A time varying filter approach for empirical mode decomposition[J].Signal Processing,2017,138:146-158.
[10] 林近山,窦春红,寇兴磊.基于时变滤波经验模式分解的齿轮箱故障诊断[J].机械传动,2018(1):98-101.LIN Jinshan,DOU Chunhong,KOU Xinglei.Gearbox fault diagnosis based on time-varying filtering empirical mode decomposition [J].Mechanical Transmission,2018 (1):98-101.
[11] ZHANG X,LIU Z,MIAO Q,et al.An optimized time varying filtering based empirical mode decomposition method with grey wolf optimizer for machinery fault diagnosis[J].Journal of Sound Vibration,2018,418:55-78.
[12] 石春香,李胡生.基于Hilbert边际谱与随机-模糊统计原理的梁桥损伤识别试验研究[J].振动与冲击,2011,30(8):123-127.SHI Chunxiang,LI Husheng.Experimental study on damage identification of beam bridge based on Hilbert marginal spectrum and random fuzzy statistics theory [J].Journal of Vibration and Shock,2011,30 (8):123-127.
[13] 祁树锋,李夕海,韩绍卿,等.基于Hilbert谱区域能量比的核爆与雷电电磁脉冲识别[J].振动与冲击,2013,32(3):163-166.QI Shufeng,LI Xihai,HAN Shaoqing,et al.Discrimination of nuclear explosion and lightning electromagnetic pulse using regional energy ratio of hilbert spectrum [J].Journal of Vibration and Shock,2013,32 (3):163-166.
[14] 邓飞跃,唐贵基,王晓龙.谐波分解结合自互补 Top-Hat变换的轴承微弱故障特征提取方法[J].振动工程学报,2015,28(6):981-989.DENG Feiyue,TANG Guiji,WANG Xiaolong.Harmonic decomposition combined with self complementary Top-Ha transform method of bearing weak fault feature extraction [J].Journal of Vibration Engineering,2015,28 (6):981-989.
[15] 胡爱军,南冰.基于自适应概率主成分分析的滚动轴承故障特征增强方法[J].振动与冲击,2017,36(19):145-150.HU Aijun,NAN Bing.Fault feature enhancement method of rolling bearing based on adaptive probabilistic principal component analysis [J].Journal of Vibration and Shock,2017,36 (19):145-150.
[16] 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 aircraft overall parameters based on particle swarm optimization[J].Journal of Aeronautics,2008,29 (6):1538-1541.