多尺度形态滤波在行星轮轴承故障诊断的应用

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (10) : 103-111.

论文

刘志强1，龚廷恺2，陈萍3，石开1

作者信息 +

Application of multiscale morphological filter in fault diagnosis of planetary bearing

LIU Zhiqiang1，GONG Tingkai2，CHEN Ping3，SHI Kai1

Author information +

文章历史 +

摘要

齿轮箱中滚动轴承的故障信号微弱且易受干扰，导致轴承故障特征提取困难。为了提取信号中的微弱故障特征，提出了一种自适应的多尺度广义形态滤波(adaptive multiscale generalized morphological filter, AMGMF)，特有的形态变换在抑制干扰的同时还增强了特征提取能力。首先，针对差值形态算子的滤波缺陷，提出了广义的优化差分算子(generalized enhanced different filter, GEDIF)，并通过幅频特性和脉冲提取特性揭示其滤波特点。其次，由局部信号特征确定扁平结构元素的长度，改进的长度选择方法确保了形态滤波的自适应性和准确性。最后，以特征幅值比(feature amplitude ratio, FAR)分配各尺度的权重，加权重构得到了AMGMF的处理结果。通过仿真信号和行星轮轴承故障信号分析，结果表明AMGMF方法能有效地从复杂信号中分离出故障特征。与单尺度形态滤波、多尺度形态滤波和EEMD对比，AMGMF方法具有一定的优越性。

Abstract

The fault signal of rolling element bearing in gearbox is weak and easy to be interfered, which leads to the difficulty of bearing fault feature extraction. In order to extract the weak fault feature in the signal, an adaptive multiscale generalized morphological filter (AMGMF) is proposed. The morphological transformation can suppress interference and enhance the ability of feature extraction. Firstly, aiming at defects of the difference filter, a generalized enhanced difference filter (GEDIF) is proposed. Amplitude frequency characteristics and pulse extraction characteristics are analyzed to reveal attributes of GEDIF. Secondly, lengths of flat structure elements are determined using local signal characteristics. Improved method of lengths selection can ensure adaptability and accuracy of morphological filter. Finally, weighted coefficients of structure elements are allocated with feature amplitude ratio (FAR). The result of AMGMF is obtained after weighted reconstruction. Through analyses of simulated signal and planetary bearing fault signal, the results show that AMGMF can effectively separate fault features from complex signals. Compared with single scale morphological filter, multiscale morphological filter and EEMD, AMGMF has certain advantages.

导出引用

刘志强1，龚廷恺2，陈萍3，石开1. 多尺度形态滤波在行星轮轴承故障诊断的应用[J]. 振动与冲击, 2023, 42(10): 103-111

LIU Zhiqiang1，GONG Tingkai2，CHEN Ping3，SHI Kai1. Application of multiscale morphological filter in fault diagnosis of planetary bearing[J]. Journal of Vibration and Shock, 2023, 42(10): 103-111

参考文献

[1] 王建国,范业锐,张文兴,等.改进差分滤波器在轴承故障诊断中的应用研究[J].振动与冲击,2019,38(13):81-86.
WANG Jianguo, FAN Yerui, ZHANG Wenxing, et al. Application of improved difference filter in bearing fault diagnosis[J]. Journal of Vibration and Shock, 2019, 38(13): 81-86.
[2] 陈平,李庆民.基于数学形态学的数字滤波器设计与分析[J].中国电机工程学报,2005,25(11):60-65.
CHEN Ping, LI Qingmin. Design and analysis of mathematical morphology-based digital filters[J]. Proceedings of the CSEE, 2005, 25(11): 60-65.
[3] 丑永新,张爱华,顾亚,等.一种快速的数学形态学滤波方法及其在脉搏信号处理中的应用[J].仪器仪表学报,2020,41(02):253-262.
CHOU Yongxin, ZHANG Aihua, GU Ya, et al. Fast mathematical morphological filtering method and its application on pulse signal processing[J]. Chinese Journal of Scientific Instrument, 2020, 41(02): 253-262.
[4] NIKOLAOU N G, ANTONIADIS A. Application of morphological operators and envelope extractions for impulsive-type periodic signals[J]. Mechanical Systems and Signals Processing, 2003, 17(6): 1147-1162.
[5] 章立军,杨德斌,徐金梧,等.基于数学形态滤波的齿轮故障特征提取方法[J].机械工程学报,2007(02):71-75.
ZHANG Lijun, YANG Debin, XU Jinwu, et al. Approach to extracting gear fault feature based on mathematical morphological filtering[J]. Journal of Mechanical Engineering, 2007(02): 71-75.
[6] DONG Y B, LIAO M F, ZHANG X L, et al. Faults diagnosis of rolling element bearings based on modified morphological method[J]. Mechanical Systems and Signal Processing, 2011, 25: 1276-1286.
[7] RAJ A S, MURALI N. Early classification of bearing faults using morphological operators and fuzzy inference[J]. IEEE Transactions on Industrial Electronics, 2013, 60: 567-574.
[8] 余建波,李传锋,吕靖香.轴承故障信号的平均组合差值形态滤波分析[J].浙江大学学报(工学版),2018,52(10):1845-1853+1910.
YU Jianbo, LI Chuanfeng, LV Jingxiang. Average combination difference morphological filter analysis on fault signals of rolling bearing[J]. Journal of Zhejiang University(engineering science), 2018,52(10):1845-1853+1910.
[9] 苗宝权,陈长征,罗园庆,等.基于自适应增强差分积形态滤波器的滚动轴承故障特征提取方法[J].机械工程学报,2021,57(09):78-88.
MIAO Baoquan, CHEN Changzheng, LUO Yuanqing, et al. Rolling bearing fault feature extraction method based on adaptive enhanced difference product morphological filter[J]. Journal of Mechanical Engineering, 2021, 57(09): 78-88.
[10] ZHANG L J, XU J W, YANG J H, et al. Multiscale morphology analysis and its application to fault diagnosis[J]. Mechanical Systems and Signal Processing, 2008, 22: 597–610.
[11] LI B, ZHANG P L, WANG Z J, et al. Gear fault detection using multi-scale morphological filters[J]. Measurement, 2011, 44: 2078-2089.
[12] DENG F Y, YANG S P, TANG G J, et al. Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing[J]. Measurement Science and Technology, 2017, 28(4): 045011.
[13] 郑直,姜万录.自适应多尺度形态差值滤波及其在液压泵故障诊断中的应用[J].机床与液压,2019,47(17):206-211.
ZHENG Zhi, JIANG Wanlu. Adaptive multi-scale morphological difference filter and its application in fault diagnosis of hydraulic pump[J]． Machine Tool ＆ Hydraulics, 2019, 47(17): 205-211.
[14] 韩笑乐,胡天中,余建波.基于自适应加权多尺度组合形态滤波的轴承故障特征提取研究[J].振动与冲击,2020,39(01):245-252.
HAN Xiaole, HU Tianzhong, YU Jianbo. Feature extraction of bearing faults based on adaptive weighted multi-scale combination morphological filtering[J]. Journal of Vibration and Shock, 2020, 39(01): 245-252.
[15] MATAGOS P, SCHAFER R W. Morphological filters-Part I: Their set-theoretic analysis and relations to liner shift-invariant filters［C］. IEEE Transactions on Acoustics Speech and Signal Processing ASSP, 1987, 35(8): 1153-1169.
[16] MATAGOS P, SCHAFER R W. Morphological filters-Part II: Their relations to median,order-statistic,and stack filters［C］. IEEE Transactions on Acoustics Speech and Signal Processing ASSP, 1987, 35(8): 1170-1184.
[17] 胡爱军,孙敬敬,向玲.振动信号处理中数学形态滤波器频率响应特性研究[J].机械工程学报,2012,48(1):98-103.
HU Aijun, SUN Jingjing, XIANG Ling. Analysis of morphological filter’s frequency response characteristics in vibration signal processing[J]. Journal of Mechanical Engineering, 2012, 48(1): 98-103.
[18] 鄢小安,贾民平.参数优化的组合形态-hat变换及其在风力发电机组故障诊断中的应用[J].机械工程学报,2016,52(13):103-110.
YAN Xiaoan, JIA Minping. Parameter optimized morphological filter-hat transform and its application in fault diagnosis of wind turbine[J]. Journal of Mechanical Engineering, 2016, 52(13): 103–110.
[19] 张西宁,唐春华,周融通,等.一种自适应形态滤波算法及其在轴承故障诊断中的应用[J].西安交通大学学报,2018,52(12):1-8+37.
ZHANG Xining, TANG Chunhua, ZHOU Rongtong, et al. Adaptive morphological filtering algorithm with applications in bearing fault diagnosis[J]. Journal of Xi’an Jiaotong University, 2018, 52(12): 1-8+37.
[20] HU A J, XIANG L. Selection principle of mathematical morphological operators in vibration signal processing[J]. Journal of Vibration and Control, 2016, 22(14): 3157-3168.
[21] HU A J, XIANG L, XU S, et al. Frequency loss and recovery in rolling bearing fault detection[J]. Chinese Journal of Mechanical Engineering, 2019, 32(1): 1-12.
[22] FENG Z P, MA H Q, ZUO M J. Vibration signal models for fault diagnosis of planet bearings[J]. Journal of Sound and Vibration, 2016, 370: 372-393.
[23] 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+153.
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+153.