形态滤波与平移不变量小波增强EEMD的故障诊断方法

摘要: 针对集成经验模式分解（Ensemble empirical mode decomposition，EEMD）在轴承故障特征提取中的问题，提出一种混合故障诊断方法。首先，将"形态滤波-平移不变量小波"作为EEMD的前置滤波器，实现对原始信号中窄带脉冲和随机噪声干扰的有效消除；其次，针对本征模式分量（Intrinsic mode functions，IMFs）中真实分量的选取问题，提出一种轴承振动信号EEMD分解的筛选规则，即计算各IMFs和原信号的自相关函数并作归一化处理，然后计算各IMFs自相关函数和原信号自相关函数的相关系数，以最大相关系数的一半作为阈值剔除虚假的IMFs，与此同时保留第1和第2阶IMFs，从而实现对EEMD的改进。仿真和实验轴承故障诊断研究表明了该方法的有效性，方法的优点在于：将"形态滤波-平移不变量小波"作为集成经验模式分解的前置滤波器，可有效去除故障轴承振动信号中的窄带脉冲和随机噪声干扰；本文的筛选规则可有效选取去噪信号EEMD分解后的IMFs中真实分量，从而可靠地获取故障特征频率。本研究为轴承故障诊断提供了一种新的手段。
- 形态滤波 /
- 平移不变量小波 /
- 降噪 /
- 改进的EEMD /
- 特征提取
Abstract: In order to solve the problem in the usage of the ensemble empirical mode decomposition (EEMD) to extract fault feature in rolling element bearing, a hybrid fault diagnosis method is proposed. First, the morphological filter combining with translation invariant wavelet are served as the pre-filter of the EEMD to effectively eliminate the narrowband pulse and random noises from original signals. Second, aiming to the reasonable selection of the true components from intrinsic mode functions (IMFs), a screening rule is proposed to improve EEMD by selecting IMFs from the vibration signals of rolling element bearings. The autocorrelation functions of both the original signal and each IMF are calculated and normalized. All correlation coefficients are obtained by calculating the autocorrelation functions of the original signal with the autocorrelation functions of each IMF. To eliminate the false IMFs, a hard threshold is defined using the half of the maximum correlation coefficient. Therefore, inspect whether the first two IMFs are reserved, if not, reserving the first two IMFs simultaneously. Simulation and experimental investigations for the fault detection of rolling bearing show that the present hybrid method is effective. The advantages of the present method are:the usage of the pre-filter of the hybrid morphological filter and translation invariant wavelet is efficient to eliminate the interference of the narrowband pulse and the random noises from the rolling element bearings with faults; the real IMFs will be effectively selected form the de-noised signal using EEMD decomposition, and finally the feature frequency will be reliably obtained. This study provides a new way to detect faults of rolling element bearings.
- morphological filter /
- translation invariant wavelet /
- denoising /
- improved EEMD /
- feature extraction

HTML全文

参考文献(21)

[1]	邱爱中,张海泉,崔晓,等.双树复小波滤波器构造及其在机械故障诊断中的应用[J].机械科学与技术,2017,36(4):542-546 Qiu A Z, Zhang H Q, Cui X, et al. Design method of DT-CWr and Its application in mechanical fault diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2017,36(4):542-546(in Chinese)
[2]	王雪冬,赵荣珍,邓林峰,等.局部费歇尔判别分值在滚动轴承故障诊断中的应用[J].机械科学与技术,2017,36(2):273-278 Wang X D, Zhao R Z, Deng L F, et al. Application of localized fisher discriminant score in fault diagnosis of rolling bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2017,36(2):273-278(in Chinese)
[3]	向家伟,崔向欢,王衍学,等.轴承故障诊断的最优化随机共振方法分析[J].农业工程学报,2014,30(12):50-55 Xiang J W, Cui X H, Wang Y X, et al. Optimized stochastic resonance method for bearing fault diagnosis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(12):50-55(in Chinese)
[4]	Wang Y X, Xiang J W, Mo Q Y, et al. Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis[J]. Measurement, 2015,68:70-81
[5]	Wang Y X, Xiang J W, Markert R, et al. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines:a review with applications[J]. Mechanical Systems and Signal Processing, 2016,66-67:679-698
[6]	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 of the Royal Society A, 1998,454(1971):903-995
[7]	Lei Y G, Li N P, Lin J, et al. Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition[J]. Sensors, 2013,13(12):16950-16964
[8]	Wu Z H, Huang N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009,1(1):1-41
[9]	Lei Y G, Lin J, He Z J, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery[J]. Mechanical Systems and Signal Processing, 2013,35(1-2):108-126
[10]	胥永刚,何正嘉,王太勇.基于经验模式分解的包络解调技术及其应用[J].西安交通大学学报,2004,38(11):1169-1172,1185 Xu Y G, He Z J, Wang T Y. Envelope demodulation method based on empirical mode decomposition with application[J]. Journal of Xi'an Jiaotong University, 2004,38(11):1169-1172,1185(in Chinese)
[11]	林近山.基于EEMD和Hilbert变换的齿轮箱故障诊断[J].机械传动,2010,34(5):62-64,75 Lin J S. Gearbox fault diagnosis based on EEMD and Hilbert transform[J]. Journal of Mechanical Transmission, 2010,34(5):62-64,75(in Chinese)
[12]	陈隽,李想.运用总体经验模态分解的疲劳信号降噪方法[J].振动、测试与诊断,2011,31(1):15-19 Chen J, Li X. Application of ensemble empirical mode decomposition to noise reduction of fatigue signal[J]. Journal of Vibration, Measurement & Diagnosis, 2011,31(1):15-19(in Chinese)
[13]	柏林,刘小峰,秦树人.小波-形态-EMD综合分析法及其应用[J].振动与冲击,2008,27(5):1-4,35 Bo L, Liu X F, Qin S R. Hybrid wavelet-morphology-EMD analysis and its application[J]. Journal of Vibration and Shock, 2008,27(5):1-4,35(in Chinese)
[14]	唐贵基,蔡伟,胡爱军,等.平移不变量小波阈值去噪法在齿轮振动信号处理中的应用[J].噪声与振动控制,2008,28(6):17-19,27 Tang G J, Cai W, Hu A J, et al. Application of translation invariant wavelet threshold denoising method in vibration signal processing of gears[J]. Noise and Vibration Control, 2008,28(6):17-19,27(in Chinese)
[15]	滕军,朱焰煌,卢伟,等.自适应分解层数的小波域中值滤波振动信号降噪法[J].振动与冲击,2009,28(12):58-62 Teng J, Zhu Y H, Lu W, et al. Vibration signal denoising method based on median filter in wavelet domain of self-adaptive decomposition level[J]. Journal of Vibration and Shock, 2009,28(12):58-62(in Chinese)
[16]	郝如江,卢文秀,褚福磊.形态滤波在滚动轴承故障声发射信号处理中的应用[J].清华大学学报(自然科学版),2008,48(5):812-815 Hao R J, Lu W X, Chu F L. Morphology filters for analyzing roller bearing fault using acoustic emission signal processing[J]. Journal of Tsinghua University (Science and Technology), 2008,48(5):812-815(in Chinese)
[17]	沈路,杨富春,周晓军,等.基于改进EMD与形态滤波的齿轮故障特征提取[J].振动与冲击,2010,29(3):154-157 Shen L, Yang F C, Zhou X J, et al. Gear fault feature extraction based on improved EMD and morphological filter[J]. Journal of Vibration and Shock, 2010,29(3):154-157(in Chinese)
[18]	Lei Y G, He Z J, Zi Y Y. EEMD method and WNN for fault diagnosis of locomotive roller bearings[J]. Expert Systems with Applications, 2011,38(6):7334-7341
[19]	刘畅,周川,伍星,等.基于广义形态滤波和相关系数的Hilbert-Huang变换方法[J].机械科学与技术,2011,30(1):71-75 Liu C, Zhou C, Wu X, et al. An improved Hilbert-Huang transform based on generalized morphological filter and correlation coefficients[J]. Mechanical Science and Technology for Aerospace Engineering, 2011,30(1):71-75(in Chinese)
[20]	陈仁祥,汤宝平,吕中亮.基于相关系数的EEMD转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-546 Chen R X, Tang B P, Lv Z L. Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system[J]. Journal of Vibration, Measurement & Diagnosis, 2012,32(4):542-546(in Chinese)
[21]	胡爱军,唐贵基,安连锁.基于数学形态学的旋转机械振动信号降噪方法[J].机械工程学报,2006,42(4):127-130 Hu A J, Tang G J, An L S. De-noising technique for vibration signals of rotating machinery based on mathematical morphology filter[J]. Chinese Journal of Mechanical Engineering, 2006,42(4):127-130(in Chinese)