变转速齿轮箱复合故障的自适应时变滤波分析

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振动与冲击 ›› 2019, Vol. 38 ›› Issue (14) : 138-145.

论文

陈向民黎琦张亢晋风华李录平

作者信息 +

Adaptive time-varying filtering method for gearbox compound fault diagnosis under variable rotational speed

CHEN Xiangmin Li Qi ZHANG Kang JING Fenghua LI Luping

Author information +

文章历史 +

摘要

复合故障诊断是机械故障诊断领域的一大难点。齿轮箱出现复合故障时，受传递路径、测点布置等影响，所拾取的复合故障振动信号中，各故障成分会呈现强弱不平衡，特别在变转速条件下，故障特征具有时变特性。因此，针对变转速下的齿轮箱复合故障诊断，提出了一种基于频域滤波的自适应时变滤波方法。该方法先在频域构建自适应时变滤波器，然后采用自适应时变滤波器将包含齿轮故障特征的时变滤波信号从齿轮箱复合故障信号中分离出来，并进行包络阶次谱分析，以提取齿轮故障特征；同时，对残余信号（齿轮箱复合故障信号与时变滤波信号的差值）进行包络阶次谱分析，以提取轴承故障特征。算法仿真和应用实例表明，自适应时变滤波方法可有效分离变转速下齿轮和滚动轴承的故障特征。

Abstract

Compound fault diagnosis is a hard problem in mechanical fault diagnosis.When compound fault occurs in a gearbox, strength imbalances between fault signal components in the measured compound fault vibration signal will appear usually, which is affected by the transfer path, measuring point arrangement and so on.Especially when the gearbox runs with variable rotational speed, the fault feature is of time-varying characteristics.Therefore, aiming at compound fault diagnosis for gearboxes operating with variable rotational speed, a frequency-domain adaptive time-varying filtering method was proposed.In the proposed method, an adaptive time-varying filter was designed in frequency-domain, and the time-varying filtered signal carrying gear fault characteristics was extracted from the compound fault signal of gearbox by using the adaptive time-varying filter, which in turn by the envelope order spectrum analysis, the fault features of the gears were extracted.Meanwhile, by the envelope order spectrum analysin on the residual signal, which is the difference between the gearbox compound fault signal and time-varying filtered signal, the fault features of the bearings were extracted.Simulations and application examples indicate that the proposed adaptive time-varying filtering method can effectively separate the fault features of the of gears and rolling bearings running with variable rotational speed.

导出引用

陈向民黎琦张亢晋风华李录平 . 变转速齿轮箱复合故障的自适应时变滤波分析[J]. 振动与冲击, 2019, 38(14): 138-145

CHEN Xiangmin Li Qi ZHANG Kang JING Fenghua LI Luping . Adaptive time-varying filtering method for gearbox compound fault diagnosis under variable rotational speed[J]. Journal of Vibration and Shock, 2019, 38(14): 138-145

参考文献

[1] 王国彪，何正嘉，陈雪峰，等. 机械故障诊断基础研究“何去何从”[J]. 机械工程学报，2013，49(1): 63-72.
Wang Guobiao, He Zhengjia, Chen Xuefeng, et al. Basic research on machinery fault diagnosis—What is the prescription[J]. Journal of Mechanical Engineering, 2013, 49(1)：63-72.
[2] 张可，周东华，柴毅. 复合故障诊断技术综述[J]. 控制理论与应用，2015，32(9): 1143-1157.
Zhou Ke, Zhou Donghua, Chai Yi. Review of multiple fault diagnosis methods[J]. Control Theory & Applications, 2015, 32(9): 1143-1157.
[3] Luo Chunyan, Shen Changqing, Fan Wei，et al. Research on the sparse representation for gearbox compound fault features using wavelet bases[J]. Shock and Vibration, 2015, (4): 1-11.
[4] 明安波，褚福磊，张炜．滚动轴承复合故障特征分离的小波－频谱自相关方法[J]．机械工程学报，2013，49(3): 80-87．
Ming Anbo, Chu Fulei, Zhang Wei. Compound fault features separation of rolling element bearing based on the wavelet decomposition and spectrum auto-correlation[J]. Journal of Mechanical Engineering, 2013, 49(3): 80-87．
[5] 王晓冬，何正嘉，訾艳阳. 多小波自适应构造方法及滚动轴承复合故障诊断研究[J]. 振动工程学报，2010，23(4): 438-444.
Wang Xiaodong, He Zhengjia, Zi Yanyang. Adaptive construction of multiwavelet and research on composite fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering, 2010, 23(4): 438-444.
[6] 崔玲丽，高立新，张建宇，等. 基于EMD 的复合故障诊断方法[J]. 北京科技大学学报，2008，30 (9): 1055-1060.
Cui Lingli, Gao Lixin, Zhang Jianyu, et al. Composite fault diagnosis method based on empirical mode decomposition[J]. Journal of University of Science and Technology Beijing, 2008, 30 (9): 1055-1060.
[7] 杨宇，罗鹏，程军圣，等. 广义变分模态分解及其在齿轮箱复合故障诊断中的应用[J]. 中国机械工程，2017，28 (9): 1069-1073.
Yang Yu, Luo Peng, Cheng Junsheng, et al. GVMD and its applications in composite fault diagnosis for gearboxes[J]. China Mechanical Engineering, 2017, 28 (9): 1069-1073.
[8] 李辉，郑海起，唐力伟. 形态分量分析在齿轮箱复合故障诊断中的应用[J]. 振动、测试与诊断，2013，33 (4): 620-626.
Li Hui, Zheng Haiqi, Tang Liwei. Application of morphological component analysis to gearbox compound fault diagnosis[J]. Journal of Vibration, Measurement & Diagnosis, 2013, 33 (4): 620-626.
[9] 杨杰，郑海起，关贞珍，等. 基于核形态成分分析的齿轮箱复合故障诊断研究[J]. 振动与冲击，2012，31(10): 97-101.
Yang Jie, Zheng Haiqi, Guan Zhenzhen, et al. Compound fault diagnosis for gearbox based on kernel morphological component analysis[J]. Journal of Vibration and Shock, 2012, 31(10): 97-101.
[10] Yu Dejie, Wang Min, Chen Xiangmin. A method for the compound fault diagnosis of gearboxes based on morphological component analysis[J]. Measurement, 2016, 91: 519-531.
[11] 田昊，唐力伟，田广. 基于盲源分离的齿轮箱复合故障诊断研究[J]. 兵工学报，2010，31 (5): 646-649.
Tian Hao, Tang Liwei, Tian Guang. Compound fault diagnosis for gearbox based on blind source separation[J]. Acta Armamentar II, 2010, 31 (5): 646-649.
[12] Yang Wenxian, Tavner P J, Court R. An online technique for condition monitoring the induction generators used in wind and marine turbines[J]. Mechanical Systems and Signal Processing, 2013, 38(1): 103-112.
[13] Urbanek J, Barszcz T, Antoni J. A two-step procedure for estimation of instantaneous rotational speed with large fluctuations[J]. Mechanical Systems and Signal Processing, 2013, 38(1): 96-102.
[14] 林京，赵明. 变转速机械设备动态信号分析方法的回顾与展望[J]. 中国科学：技术科学，2015，45(7): 669-686.
Lin Jing，Zhao Ming. Dynamic signal analysis for speed-varying machinery: A review[J]. Scientia Sinica(Technologica), 2015, 45(7): 669–686.
[15] 郑近德，潘海洋，杨树宝，等. 广义变分模态分解方法及其在变工况齿轮故障诊断中的应用[J]. 振动工程学报，2017，30(3): 502-509.
Zheng Jinde, Pan Haiyang, Yang Shubao, et al. Generalized variational mode decomposition and its applications to gearbox fault diagnosis under variable conditions[J]. Journal of Vibration Engineering, 2017, 30(3): 502-509.
[16] Saavedra P N, Rodriguez C G. Accurate assessment of computed order tracking[J]. Shock & Vibration, 2006, 13 (1):13-32.
[17] Feng Zhipeng, Liang Ming, Chu Fulei. Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples[J]. Mechanical Systems and Signal Processing, 2013, 38: 165–205.
[18] 郭瑜，秦树人，汤宝平，等. 基于瞬时频率估计的旋转机械阶比跟踪[J]. 机械工程学报，2003，39(3): 32-36.
Guo Yu, Qin Shuren, Tang Baoping, et al. Order tracking of rotating machinery based on instantaneous frequency estimation[J]. Journal of Mechanical Engineering, 2003, 39(3): 32-36.
[19] Zou J, Chen J. A comparative study on time–frequency feature of cracked rotor by Wigner–Ville distribution and wavelet transform[J]. Journal of Sound and Vibration, 2004, 276(1-2): 1-11.
[20] 郭远晶，魏燕定，周晓军，等. 基于 S 变换谱阈值去噪的冲击特征提取方法[J]. 振动与冲击，2014，33(21)：44-50.
Guo Yuanjing, Wei Yanding, Zhou Xiaojun, et al. An impact feature extracting method based on S-transformation spectrum threshold denoising[J]. Journal of Vibration and Shock, 2014, 33(21)：44-50.
[21] 滕伟，安宏文，马志勇，等. 基于时频滤波的汽轮机半速涡动故障成分提取[J]. 振动与冲击，2015，34(3)：178-182.
Teng We, An Hongwen, Ma Zhiyong, et al. Semi-speed oil whirl fault component extraction in a steam turbine based on time-frequency filtering[J]. Journal of Vibration and Shock, 2015, 34(3): 178-182.
[22] 彭富强，于德介，武春燕. 基于自适应时变滤波阶比跟踪的齿轮箱故障诊断[J]. 机械工程学报，2012，48(7)：77-85.
Peng Fuqiang, Yu Dejie, Wu Chunyan. Self-adaptively time-varying filter based order tracking method and its application in gearbox fault diagnosis[J]. Journal of Mechanical Engineering, 2012, 48(7): 77-85.
[23] Candès E J, Charlton P R, Helgason H. Detecting highly oscillatory signals by chirplet path pursuit[J]. Applied and Computational Harmonic Analysis, 2008, 24(1): 14-40．
[24] Wu Chunyan，Liu Jian，Peng Fuqiang，et al. Gearbox fault diagnosis using adaptive zero phase time-varying filter Based on multi-scale chirplet sparse signal decomposition[J]. Chinese Journal of Mechanical Engineering, 2013, 26(4): 831-838.
[25] 丁康，李巍华，朱小勇. 齿轮及齿轮箱故障诊断实用技术[M]. 北京：机械工业出版社，2005.