基于VMD和SVDD结合的滚动轴承性能退化程度定量评估

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摘要提出了一种基于变分模态分解(VMD)和支持向量数据描述(SVDD)相结合的滚动轴承性能退化程度定量评估方法。针对当采样时间长、采集到的信号数据点多时，信号中某些部分可能受到异常信号干扰的问题，首先提出了一种基于VMD和SVDD结合的特征提取新方法，将长信号分为多帧短信号，分别使用VMD方法分解短信号并提取各分量的奇异值组成特征向量，得到一组特征向量集，然后使用SVDD方法找到并剔除其中的异常样本点，求出剩余特征向量的平均值便可作为原信号的特征。特征提取完毕后，使用SVDD方法进行性能退化评估，以待检样本到训练得到的超球体模型球心的距离描述性能退化程度，并使用隶属函数将距离指标转化为与正常状态的隶属度作为性能退化指标，实现设备的性能退化程度的定量评估。使用轴承全寿命数据，并与以传统时域无量纲指标作为特征的分析结果进行了对比，验证了所提出方法的优越性。

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	姜万录1
	2
	雷亚飞1
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	韩可1
	2
	3
	张生1
	2
	苏晓1
	2

关键词 ：变分模态分解, 支持向量数据描述, 性能退化评估, 异常点检测

Abstract：A performance degradation degree quantitative assessment method for rolling bearings was proposed, which integrates the methods of Variational Mode Decomposition(VMD) and Support Vector Data Description (SVDD).Aiming at solving the problem that some parts of a signal may be disturbed by abnormal signals if the sampling time is long and the collected signal contains too many data points.A new feature extraction method based on VMD and SVDD was proposed, in which the long signal was segmented into several short frame signals, and the short signals were decomposed by VMD to obtain several components.The singular value of each component was extracted respectively to form a feature vector, and then a set of feature vectors was obtained.After finding and removing outliers by SVDD, the average value of the remained feature vectors was used as the feature of the original signal.Following the feature extraction, SVDD was used to assess the performance degradation.The degree of performance degradation was described by the distance from the test sample to the center of the hypersphere model, and the membership function was used to transform the distance index into the membership degree to the normal state and taken as the performance degradation index, which quantitatively assesses the performance degradation degree.The proposed method was tested with the complete life data of a rolling bearing, and the result was compared with the analysis result by the traditional time-domain index feature extraction method.The superiority of the proposed method was verified.

Key words： Variational Mode Decomposition Support Vector Data Description performance degradation assessment outlier detection

收稿日期: 2017-05-25 出版日期: 2018-11-15

引用本文:

姜万录1,2，雷亚飞1,2，韩可1,2,3，张生1,2，苏晓1,2. 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.
JIANG Wan-lu1,2, LEI Ya-fei1,2, HAN Ke1,2,3, ZHANG Sheng1,2, SU Xiao1,2. Performance degradation quantitative assessment method for rolling bearings based on VMD and SVDD. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(22): 43-50.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I22/43

[1] 周川, 伍星, 刘畅, 等. 基于EMD和模糊C均值聚类的滚动轴承故障诊断[J]. 昆明理工大学学报自然科学版, 2009, 34(6): 34-39.
ZHOU Chuan, WU Xing, LIU Chang, et al. Rolling bearing fault diagnosis based on EMD and fuzzy C means clustering[J]. Journal of Kunming University of Science and Technology(Natural Science Edition), 2009, 34(6): 34-39.
[2] 彭畅, 柏林, 谢小亮. 基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J]. 振动与冲击, 2012, 31(20): 143-146.
PENG Chang, BO Lin, XIE Xiao-liang. Fault diagnosis method of rolling element bearings based on EEMD, measure-factor and fast kurtogram[J]. Journal of Vibration and Shock, 2012, 31(20): 143-146.
[3] 王恒, 马海波, 徐海黎, 等. 机械设备性能退化评估与预测研究综述[J]. 机械强度, 2013(6): 716-723.
WANG Heng, MA Hai-bo, XU Hai-li, et al. Review on machinery performance degradation assessment and prognostics[J]. Journal of Mechanical Strength, 2013(6): 716-723.
[4] Huang N E, Zheng S, Steven R, et al. The empirical mode de-composition and the Hilbert spectrum for nonlinear non-stationary time series analysis. Proceedings: Mathematical, Physical and Engineering Sciences[C]. London, The Royal Society Press, 454(1971), 1998: 903-995.
[5] 王增才, 王树梁, 任锴胜,等. 基于EEMD的提升机天轮轴承故障诊断方法[J]. 煤炭学报, 2012, 37(4): 689-694.
WANG Zeng-cai, WANG Shu-liang, REN Kai-sheng, et al. Research on the method of hoist head sheave bearing fault diagnosis based on EEMD[J]. Journal of China Coal Society, 2012, 37(4): 689-694.
[6] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[7] Tax D M J, Duin R P W. Support vector domain description[J]. Pattern Recognition Letters, 1999, 20(11-13): 1191-1199.
[8] Vapnik V N. The Nature of Statistical Learning Theory[J]. IEEE Transactions on Neural Networks, 1997, 8(6): 1564.
[9] 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.
LI Ling-jun, ZHANG Zhou-suo, HE Zheng-jia. Research of mechanical system fault diagnosis based on support vector data description[J]. Journal of Xi'an Jiaotong University, 2003, 37(9): 910-913.
[10] 刘雨, 陈进, 潘玉娜,等. 基于SVDD与信息融合技术的设备性能退化评估[J]. 振动与冲击, 2009, 28(9): 21-24.
LIU Yu, CHEN Jin, PAN Yu-na, et al. Equipment performance degradation assessment based on SVDD and information fusion technology[J]. Journal of Vibration and Shock, 2009, 28(9): 21-24.
[11] 李国宾, 关德林, 李廷举. 基于小波包变换和奇异值分解的柴油机振动信号特征提取研究[J]. 振动与冲击, 2011, 30(8): 149-152.
LI Guo-bin, GUAN De-lin, LI Ting-ju. Study on Feature Extraction of Diesel Engine Vibration Signal Based on Waveletpacket Transform and Singularity Value Decomposition[J]. Journal of Vibration and Shock, 2011, 30(8): 149-152.
[12] J. Lee, H. Qiu, G. Yu, J. Lin, and Rexnord Technical Services (2007). IMS, University of Cincinnat.