Performance Degradation Assessment Model of Rolling Bearings Combining VMD Symbol Entropy and SVDD
-
摘要: 针对滚动轴承的早期故障难以检测的问题,提出了一种基于变模态分解(Variable mode decomposition, VMD)符号熵和支持向量数据描述(Support vector data description, SVDD)的滚动轴承性能退化评估模型。首先对振动信号进行VMD分解并提取各个分量符号熵,并采用双样本Z值对各个分量符号熵进行评价,选取双样本Z值最大的特征作为特征向量。特征提取完毕后,采用SVDD模型进行性能退化评估,使用全寿命数据进行模型的验证。实验结果表明,相比于排列熵特征提取方法以及模糊C均值聚类(Fuzzy c-means clustering, FCM)方法,该模型可以更好显示出滚动轴承性能退化规律。Abstract: In order to solve the problem of early fault detection of rolling bearings, a performance degradation assessment model of rolling bearings combining variable mode decomposition (VMD) symbol entropy and support vector data description (SVDD) was proposed in study. Firstly, the vibration signal is decomposed by VMD and the symbol entropy of each component is extracted. Then, the Z value of double samples is used to evaluate the symbol entropy of each component, and the feature with the largest Z value of double samples is selected as the feature vector. After feature extraction, SVDD model is finally used to evaluate performance degradation, and life cycle data is used to verify the model. The experimental results show that this model can better show the performance degradation law of rolling bearings compared with permutation entropy (PE) feature extraction method and fuzzy c-means clustering (FCM) method.
-
表 1 不同K值下的IMF分量中心频率
K 中心频率/Hz IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 2 29 4 385 3 29 3 497 5 836 4 29 1 880 4 385 7 749 5 29 1 880 4 385 5 836 7 749 6 29 1 880 3 497 4 385 5 836 8 556 7 29 985 3 497 4 266 4 531 5 836 8 556 -
[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 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201301010.htmWANG G B, HE Z J, CHEN X F, et al. Basic research on machinery fault diagnosis-what is the prescription[J]. Journal of Mechanical Engineering, 2013, 49(1): 63-72 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201301010.htm [2] 潘玉娜. 滚动轴承的性能退化特征提取及评估方法研究[D]. 上海: 上海交通大学, 2011PAN Y N. Study on feature extraction and assessment method of rolling element bearing performance degradation[D]. Shanghai: Shanghai Jiao Tong University, 2011 (in Chinese) [3] 黄天然, 谭建平, 薛少华, 等. EMD与排列熵在提升机跳绳故障诊断中的应用[J]. 传感器与微系统, 2020, 39(7): 150-153+160 https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202007044.htmHUANG T R, TAN J P, XUE S H, et al. Application of EMD and permutation entropy in fault diagnosis of hoist wire jumping rope[J]. Transducer and Microsystem Technologies, 2020, 39(7): 150-153+160 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ202007044.htm [4] 胡爱军, 孙敬敬, 向玲. 经验模态分解中的模态混叠问题[J]. 振动、测试与诊断, 2011, 31(4): 429-434 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201104006.htmHU A J, SUN J J, XIANG L. Mode mixing in empirical mode decomposition[J]. Journal of Vibration, Measurement & Diagnosis, 2011, 31(4): 429-434 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCS201104006.htm [5] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544 doi: 10.1109/TSP.2013.2288675 [6] 姜万录, 雷亚飞, 韩可, 等. 基于VMD和SVDD结合的滚动轴承性能退化程度定量评估[J]. 振动与冲击, 2018, 37(22): 43-50 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201822007.htmJIANG W L, LEI Y F, HAN K, et al. Performance degradation quantitative assessment method for rolling bearings based on VMD and SVDD[J]. Journal of Vibration and Shock, 2018, 37(22): 43-50 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201822007.htm [7] 王斐, 房立清, 赵玉龙, 等. 基于VMD和SVDD的滚动轴承早期微弱故障检测和性能退化评估研究[J]. 振动与冲击, 2019, 38(22): 224-230+256 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201922032.htmWANG F, FANG L Q, ZHAO Y L, et al. Rolling bearing early weak fault detection and performance degradation assessment based on VMD and SVDD[J]. Journal of Vibration and Shock, 2019, 38(22): 224-230+256 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201922032.htm [8] ZHANG X, MIAO Q, ZHANG H, et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery[J]. Mechanical Systems and Signal Processing, 2018, 108: 58-72 doi: 10.1016/j.ymssp.2017.11.029 [9] 何园园, 张超, 朱腾飞. ELMD熵特征融合与PSO-SVM在齿轮故障诊断中的应用[J]. 机械科学与技术, 2019, 38(2): 271-276 doi: 10.13433/j.cnki.1003-8728.20180171HE Y Y, ZHANG C, ZHU T F. Application of ELMD entropy feature fusion and PSO-SVM in gear fault diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(2): 271-276 (in Chinese) doi: 10.13433/j.cnki.1003-8728.20180171 [10] 郭学卫, 申永军, 杨绍普. 基于样本熵和分数阶傅里叶变换的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(18): 65-69 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201718010.htmGUO X W, SHEN Y J, YANG S P. Application of sample entropy and fractional Fourier transform in the fault diagnosis of rolling bearings[J]. Journal of Vibration and Shock, 2017, 36(18): 65-69 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201718010.htm [11] 郑小霞, 周国旺, 任浩翰, 等. 基于变分模态分解和排列熵的滚动轴承故障诊断[J]. 振动与冲击, 2017, 36(22): 22-28 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201722004.htmZHENG X X, ZHOU G W, REN H H, et al. A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy[J]. Journal of Vibration and Shock, 2017, 36(22): 22-28 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201722004.htm [12] 王日新, 龚学兵, 徐敏强, 等. 飞轮系统的符号动力学故障检测方法[J]. 哈尔滨工业大学学报, 2016, 48(10): 31-38 https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201610004.htmWANG R X, GONG X B, XU M Q, et al. A symbolic dynamic analysis of flywheel system for fault detection[J]. Journal of Harbin Institute of Technology, 2016, 48(10): 31-38 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201610004.htm [13] 陈晓平, 和卫星, 马东玲, 等. 基于符号熵与支持向量机的滚动轴承故障诊断[J]. 中国机械工程, 2010, 21(17): 2079-2082 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201017015.htmCHEN X P, HE W X, MA D L, et al. Symbol entropy and SVM based rolling bearing fault diagnosis[J]. China Mechanical Engineering, 2010, 21(17): 2079-2082 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201017015.htm [14] TAX D M J, DUIN R P W. Support vector data description[J]. Machine Learning, 2004, 54(1): 45-66 [15] 周建民, 徐清瑶, 张龙, 等. 结合小波包奇异谱熵和SVDD的滚动轴承性能退化评估[J]. 机械科学与技术, 2016, 35(12): 1882-1887 doi: 10.13433/j.cnki.1003-8728.2016.1213ZHOU J M, XU Q Y, ZHANG L, et al. Assessment method of rolling bearing performance degradation based on wavelet packet singular spectral entropy and SVDD[J]. Mechanical Science and Technology for Aerospace Engineering, 2016, 35(12): 1882-1887 (in Chinese) doi: 10.13433/j.cnki.1003-8728.2016.1213 [16] 李凌均, 张周锁, 何正嘉. 基于支持向量数据描述的机械故障诊断研究[J]. 西安交通大学学报, 2003, 37(9): 910-913 https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200309008.htmLI L J, ZHANG Z S, HE Z J. Research of mechanical system fault diagnosis based on support vector data description[J]. Journal of Xi'an Jiaotong University, 2003, 37(9): 910-913 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAJT200309008.htm [17] 张豪, 陈黎飞, 郭躬德. 基于符号熵的序列相似性度量方法[J]. 计算机工程, 2016, 42(5): 201-206+212 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201605035.htmZHANG H, CHEN L F, GUO G D. Sequence similarity measurement method based on symbol entropy[J]. Computer Engineering, 2016, 42(5): 201-206+212 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201605035.htm [18] 丁闯, 冯辅周, 张兵志, 等. 改进多尺度符号动力学信息熵及其在行星变速箱特征提取中的应用[J]. 振动与冲击, 2020, 39(13): 97-102+147 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202013016.htmDING C, FENG F Z, ZHANG B Z, et al. MMSDE and its application in feature extraction of a planetary gearbox[J]. Journal of Vibration and Shock, 2020, 39(13): 97-102+147 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202013016.htm -