滚动轴承复合故障诊断的自适应方法研究

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振动与冲击 ›› 2016, Vol. 35 ›› Issue (10) : 145-150.

论文

滚动轴承复合故障诊断的自适应方法研究

马新娜1 杨绍普2

作者信息 +

Study of adaptive compound fault diagnosis of rolling bearing

Xinna Ma1 Shaopu Yang2

Author information +

文章历史 +

摘要

铁路货车滚动轴承检测中多种故障共存的情况普遍存在。针对单通道情况下复合故障难分离和诊断的问题，提出了基于陷波器的自适应复合故障诊断方法。该方法首先对滚动轴承的复合故障振动信号进行经验模态分解（empirical mode decomposition, EMD），根据各个本征模态函数与原始信号的相关系数最大化原则对信号进行重构，进一步通过频谱分析识别出滚动轴承主故障。在此基础上，利用自适应陷波器系统对原始振动信号的主故障信号进行陷波处理。然后再对筛去主故障信息的信号进行次故障诊断。通过仿真和实验分析，结果表明基于陷波器的自适应复合故障诊断方法能在一定程度上满足复合故障信号分离和故障诊断的要求，具有一定的实用性。

Abstract

In the process of bearing detection of freight car, compound faults are prevalent. Aimed at difficulty of separate the compound fault features from a single-channel vibration, the adaptive fault diagnosis based on notch filter is proposed. Firstly, original compound fault vibration signals are decomposed based on the empirical mode decomposition (EMD). Then vibration signals are reconstructed by the maximum of correlation coefficients of intrinsic mode functions (IMFS) and original signal. The primary fault can be diagnosed with spectrum analysis. Accordingly, original signal are filtered from the primary fault signals with adaptive notch filter system. Finally, subordinate fault can be diagnosed through the filtered signal. Research result of simulation and experimental signals indicate that the adaptive fault diagnosis based on notch filer can extract the primary and subordinate fault from the compound fault signals and is valuable for engineering application.

导出引用

马新娜1 杨绍普2. 滚动轴承复合故障诊断的自适应方法研究[J]. 振动与冲击, 2016, 35(10): 145-150

Xinna Ma1 Shaopu Yang2 . Study of adaptive compound fault diagnosis of rolling bearing[J]. Journal of Vibration and Shock, 2016, 35(10): 145-150

参考文献

[1] 崔玲丽，吴春光，邬娜. 基于EMD与ICA的滚动轴承复合故障诊断[J]. 北京工业大学学报，2014,40（10）：1459-1464
Cui Ling-li Wu Chun-guang Wu Na. Composite fault diagnosis of rolling bearing based on EMD and ICA algorithm[J]. Journal of beijing university of technology, 2014,40（10）：1459-1464, ( in Chinese)
[2] 唐德尧，王定晓，杨政明，等. 共振解调技术与机车车辆传动装置故障诊断[J]. 电力机车技术，2002,25（5）：1-5
Tang Deyao Wang Dingxiao, Yang Zhengming, etc. Demodulated resonance technique and tailure diagnosing of the gearing on the rolling stock[J]. Technology for Electric Locomotives, 2002, 25(5):1-5, ( in Chinese)
[3] 林京. 连续小波变换及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报，1999，33（11）：108-110
Lin Jing. Continuous wavelet transform and its application for bearing diagnosis[J]. Journal of xi’an jiaotong university, 1999, 3(11):108-110
[4] 陈彬强，张周锁，訾艳阳，何正嘉. 机械故障诊断的衍生增强离散解析小波分析框架[J]. 机械工程学报，2014,50（17）：77-86
Chen Bingqiang, Zhang Zhousuo, Zi Yangyang, He Zhengjia. Novel ensemble analytic discrete framelet expansion for machinery fault diagnosis[J]. Journal of mechanical engineering, 2014, 50(17):77-86
[5] ANTONI J. The spectral kurtosis: A useful tool for characterising non-stationary signals[J]. Mechanical systems and signal processing, 2006, 20(2):282-307
[6] 周智，朱永生，张优云，朱川峰，王鹏. 基于EEMD和共振解调的滚动轴承自适应故障诊断[J]. 振动与冲击，2013,32（2）：76-80
Zhou Zhi, Zhu Yong-sheng, Zhang You-yun, Zhu Chuan-feng, Wang Feng. Adaptive fault diagnosis of rolling bearings based on EEMD and demodulated resonance[J]. Journal of Vibration and Shock, 2013, 32(2):76-80, ( in Chinese)
[7] PATEL T H, DARPE A K. Coupled bendling-torsional vibration analysis of rotor with rub and crack[J]. Journal of sound and vibration, 2009, 326(3-5):740-752
[8] JALAN A K, MOHANTY A R. Model based fault diagnosis of a rotor-bearing system for misalignment and unbalance under steady-state condition[J]. Journal of sound and vibration, 2009, 327(3-5)604-622
[9] HONG H, LIANG M. Separation of fault features from a single-channel mechanical signal mixture using wavelet decompositon[J]. Mechanical systems and signal processing, 2007, 21(5):2025-2040
[10] ANTONI J. Blind separation of vibration components: principles and demonstrations[J]. Mechanical systems and signal processing, 2005, 19(6):1166-1180
[11] PELED R, BRAUN S, ZACHSENHOUS A. A blind deconvolution separation of multiple sources with application to bearing diagnostics[J]. Mechanical systems and sigtnal processing, 2005, 19(6):1181-1195
[12] 沈国际，陶利民，陈仲生. 多频信号经验模态分解的理论研究及应用[J]. 振动工程学报，2005,18（1）：91-94
Shen Guo-ji, Tao Li-min, Chen Zhong-sheng. Theoretical research on empirical mode decomposition of multi-frequency signal and its application[J]. Journal of vibration engineering, 2005, 18(1):91-94, ( in Chinese)
[13] 蔡艳平，李艾华，石林锁，等. 基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J]. 振动与冲击，2011,30（2）：167-172
Cai Yan-ping, Li Ai-hua, Shi Lin-suo, etc. Roller bearing fault detection using improved envelope spectrum analysis based on EMD and spetrum kurtosis[J]. Journal of vibration and shock, 2011,30(2):167-172, ( in Chinese)
[14] 胥永刚，孟志鹏，赵国亮. 基于双树复小波变换的轴承复合故障诊断研究[J]. 仪器仪表学报，2014,35（2）：447-452
Xu Yonggang, Meng Zhipeng, Zhao Guoliang. Study on compound fault diagnosis of rolling bearing based on dual-tree complex wavelet transform[J]. Chinese journal of sicentific instrument, 2014, 35(2):447-452
[15] 崔玲丽，高立新，殷海晨，胥永刚. 基于第二代小波的复合故障诊断方法研究[J]. 中国机械工程，2009,20（4）：442-446
Cui Lingli, Gao Lixin, Yin Haichen, Xu Yonggang. Research on composite fault diagnosis method based on the second generation wavelet[J]. China mechanical engineering, 2009, 20(4):442-446
[16] 张超，陈建军，郭讯. 基于第2代小波和EMMD的转子系统复合故障诊断[J]. 振动、测试与诊断，2011,31（1）：98-103
Zhang Chao, Chen Jian-jun, Guo Xun. Complex fault diagnosis for rotor systems using the second generation wavelet and extremum field mean mode decomposition[J]. Journal of vibration, measurement & diagnosis, 2011,31（1）：98-103, ( in Chinese)
[17] 储昭碧. 基于自适应陷波滤波器的电力信号时频分析. 合肥工业大学[D]，2009
Chu Zhao-bi. Adaptive notch filter-based time-frequency analysis of signals in power system. Hefei university of technology[D], 2009
[18] Mojiri M, Karimi-ghartemani M, Bakhshai A. Time-domain signal analysis using adaptive notch filter, IEEE Trans. Signal process, 2007, 55(1):85-93
[19] 张志刚，石晓辉，施全，汤宝平. 基于改进EMD和谱峭度法滚动轴承故障特征提取[J]. 振动、测试与诊断，2013, 33（3）：478-482
Zhang Zhi-gang, Shi Xiao-hui, Sha Quan, Tang Bao-ping. Fault feature extraction of rolling element bearing based on improved EMD and spectral kurtosis[J]. Journal of Vibration, Measurement & Diagnosis. 2013, 33（3）：478-482, ( in Chinese)
[20] 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 An-bo, Chu Fu-lei, 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, ( in Chinese)