改进差分滤波器在轴承故障诊断中的应用研究
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摘要
针对形态差分滤波器(Difference Filter, DIF)在信号特征提取中可能存在频率混淆的问题,提出了改进的差分滤波器(Improved Difference Filter, IDIF)的信号处理方法。由差分滤波器的内部结构特点可知,传统的形态差分滤波器无法同时识别正负冲击特性,导致频率混淆难识别;通过对滤波器的结构进行改进,利用帽变换辨别信号的主导冲击方向,提高了滤波器识别正负冲击特征的能力。为了验证该方法的有效性,对此进行了仿真实验和实际滚动轴承的故障诊断实验。实验结果表明,改进的形态差分滤波器可以同时区分出故障特征的正负特性,较好的避免频率混淆。
Aiming at the problem of morphological difference filters existing frequency confusing in signal feature extraction, an improved differential filter's signal processing method was proposed. According to characteristics of a differential filter's inner structure, it was shown that a traditional morphological difference filter can't identify positive and negative impact characteristics simultaneously to cause frequency confusion being difficult to recognize. Here, through improving the filter structure, Hat transform was used to recognize the dominant impact direction of a signal, and improve the filter's ability to identify positive and negative impact characteristics. In order to verify the effectiveness of the proposed method, Numerical simulation and actual rolling element bearing fault diagnosis tests were conducted. The results showed that the improved morphological difference filter can distinguish positive and negative characteristics of fault features simultaneously to better avoid frequency confusion.
Aiming at the problem of morphological difference filters existing frequency confusing in signal feature extraction, an improved differential filter's signal processing method was proposed. According to characteristics of a differential filter's inner structure, it was shown that a traditional morphological difference filter can't identify positive and negative impact characteristics simultaneously to cause frequency confusion being difficult to recognize. Here, through improving the filter structure, Hat transform was used to recognize the dominant impact direction of a signal, and improve the filter's ability to identify positive and negative impact characteristics. In order to verify the effectiveness of the proposed method, Numerical simulation and actual rolling element bearing fault diagnosis tests were conducted. The results showed that the improved morphological difference filter can distinguish positive and negative characteristics of fault features simultaneously to better avoid frequency confusion.
引文
[1] ZHENG Jinde,PAN Haiyang,CHENG Junsheng.Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines[J].Mechanical Systems & Signal Processing,2017,85:746-759.
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[11] LI Y F,LIANG X H,ZUO M J.Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2017,85:146-161.
[12] 姜万录,郑直,朱勇,等.基于最优扁平型结构元素长度的液压泵故障诊断研究[J],振动与冲击,2014,33(15):35-41.JIANG Wanlu,ZHENG Zhi,ZHU Yong,et al.Hydraulic pump fault diagnosis based on optimal flat structure element length[J],Journal of Vibration and Shock,2014,33(15):35-41.
[13] 李兵,高敏,张旭光,等.用形态梯度法与非负矩阵分解的齿轮故障诊断[J].振动.测试与诊断,2014,34(2):295-300.LI Bing,GAO Min,ZHANG Xuguang,et al.Feature extraction for engine fault diagnosis by utilizing adaptive multi-scale morphological gradient and non-negative matrix factorization[J].Journal of Vibration,Measurement & Diagnosis,2014,34(2):295-300.
[14] CHUAN L,MING L.Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis[J].Journal of Sound and Vibration,2012,331(26):5864-5879.
[15] LI Bing,ZHANG Peilin,WANG Zhengjun,et al.Gear fault detection using multi-scale morphological filters[J].Measurement,2011,44(10):2078-2089.
[16] GONG Tingkai,YUAN Yanbin,YUAN Xiaohui,et al.Application of optimized multiscale mathematical morphology for bearing fault diagnosis[J].Measurement Science & Technology,2017,28(4):045401.
[2] RUBINI R.Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearing[J].Mechanical Systems & Signal Processing,2001,15(2):287-302.
[3] ZHANG P,DU Y,HABETLET T G,et al.A survey of condition monitoring and protection methods for mediun voltage induction motors[J].IEEE Transactions on Industry Applications,2011,47(1):34-46.
[4] 武哲,杨绍普,任彬,等.基于噪声辅助多元经验模态分解和多尺度形态学的滚动轴承故障诊断方法[J].振动与冲击,2016,35(4):127-133.WU Zhe,YANG Shaopu,REN Bin,et al.Rolling element bearing fault diagnosis method based on NAMEMD and multi-scale morphology[J].Journal of Vibration and Shock,2016,35(4):127-133.
[5] 钱林,康敏,傅秀清,等.基于VMD的自适应形态学在轴承故障诊断中的应用[J].振动与冲击,2017,36(3):227-233.QIAN Lin,KANG Min,FU Xiuqing,et al.Application of adaptivemorphology in bearing fault diagnosis based on VMD [J].Journal of Vibration and Shock,2017,36(3):227-233.
[6] JIANG Wanlu,ZHENG Zhi,ZHU Yong,et al.Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multi-scale morphology analysis[J].Mechanical Systems & Signal Processing,2015,58/59:179-205.
[7] LI Y F,ZUO M J,LIN J H,et al.Fault detection method for railway wheel flat using an adaptive multiscale morphological filter[J].Mechanical Systems & Signal Processing,2017,84:642-658.
[8] Lü Jingxiang,YU Jianbo.Average combination difference morphological filters for fault feature extraction of bearing[J].Mechanical Systems & Signal Processing,2018,100:827-845.
[9] DENG Feiyue,YANG Shaopu,TANG Guiji,et al.Self adaptive multi-scale morphology AVG-Hat filter and its application to fault feature extraction for wheel bearing[J].Measurement Science & Technology,2017,28(4):045011.
[10] 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 Xiao’an,JIA Minping.Parameter optimized combination morphological filter-hat transform and its application in fault diagnosis of wind turbine[J].Journal of Mechanical Engineering,2016,52(13):103-110.
[11] LI Y F,LIANG X H,ZUO M J.Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis[J].Mechanical Systems and Signal Processing,2017,85:146-161.
[12] 姜万录,郑直,朱勇,等.基于最优扁平型结构元素长度的液压泵故障诊断研究[J],振动与冲击,2014,33(15):35-41.JIANG Wanlu,ZHENG Zhi,ZHU Yong,et al.Hydraulic pump fault diagnosis based on optimal flat structure element length[J],Journal of Vibration and Shock,2014,33(15):35-41.
[13] 李兵,高敏,张旭光,等.用形态梯度法与非负矩阵分解的齿轮故障诊断[J].振动.测试与诊断,2014,34(2):295-300.LI Bing,GAO Min,ZHANG Xuguang,et al.Feature extraction for engine fault diagnosis by utilizing adaptive multi-scale morphological gradient and non-negative matrix factorization[J].Journal of Vibration,Measurement & Diagnosis,2014,34(2):295-300.
[14] CHUAN L,MING L.Continuous-scale mathematical morphology-based optimal scale band demodulation of impulsive feature for bearing defect diagnosis[J].Journal of Sound and Vibration,2012,331(26):5864-5879.
[15] LI Bing,ZHANG Peilin,WANG Zhengjun,et al.Gear fault detection using multi-scale morphological filters[J].Measurement,2011,44(10):2078-2089.
[16] GONG Tingkai,YUAN Yanbin,YUAN Xiaohui,et al.Application of optimized multiscale mathematical morphology for bearing fault diagnosis[J].Measurement Science & Technology,2017,28(4):045401.