基于基本尺度熵与GG模糊聚类的轴承性能退化状态识别

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摘要针对轴承性能退化状态的识别问题，提出一种基于基本尺度熵与GG聚类的退化状态识别方法。首先分析轴承性能退化过程中的基本尺度熵演化规律，并分析该参数的单调性与敏感性。考虑到轴承退化状态在时间尺度的连续性，构建基本尺度熵、有效值以及退化时间的三维退化特征向量，并采用GG模糊聚类方法对轴承性能退化状态的不同阶段进行划分，实现对性能退化状态的识别。采用来自IEEE PHM 2012的轴承全寿命试验数据进行实例分析，并与FCM、GK算法进行对比，结果表明本文所提出的方法聚类效果更优，同一退化状态内的时间聚集度更高，能够为轴承性能退化状态的识别提供一种有效的途径。

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	王冰1
	胡雄1
	李洪儒2
	孙德建1

关键词 ：基本尺度熵, 特征提取, GG模糊聚类, 滚动轴承, 状态识别

Abstract：A method based on basic scale entropy and GG clustering was proposed to solve the problem of bearing performance degradation state recognition. The evolution law of the basic scale entropy in bearing performance degradation process was analyzed firstly, and its monotonicity and sensitivity were emphasized. Considering the continuity of bearing degradation state on time scale, a 3D degradation feature vector was constructed with basic scale entropy, its root mean square and degradation time, and GG fuzzy clustering method was used to divide different stages of bearing performance degradation state to realize bearing performance degradation state recognition. Bearing full lifetime test data of IEEE PHM 2012 was adopted to do example analysis, and the results were compared with those using FCM and GK algorithms. The results showed that the proposed method’s clustering effect is better and its time aggregation degree is higher in the same degradation state; the method can provide an effective way for bearing performance degradation state recognition.

Key words： basic scale entropy feature extraction GG fuzzy clustering rolling bearing condition recognition

收稿日期: 2017-10-26 出版日期: 2019-02-28

引用本文:

王冰1，胡雄1, 李洪儒2, 孙德建1. 基于基本尺度熵与GG模糊聚类的轴承性能退化状态识别[J]. 振动与冲击, 2019, 38(5): 190-197.
WANG Bing1, HU Xiong1, LI Hongru2, SUN Dejian1. Rolling bearing performance degradation state recognition based on basic scale entropy and GG fuzzy clustering. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(5): 190-197.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2019/V38/I5/190

[1] Vogl G W, Weiss B A, Helu M. A review of diagnostic and prognostic capabilities and best practices for manufacturing[J]. Journal of Intelligent Manufacturing, 2016:1-17.
[2] Rai A, Upadhyay S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings[J]. Tribology International, 2016, 96:289-306.
[3] López A J G, Márquez A C, Macchi M, et al. Prognostics and Health Management in Advanced Maintenance Systems[J]. Production Planning & Control, 2018:1-14.
[4] Yi C, Lv Y, Ge M, et al. Tensor Singular Spectrum Decomposition Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis[J]. Entropy, 2017, 19(4):139-145.
[5] Yi C, Lv Y, Dang Z, et al. Quaternion singular spectrum analysis using convex optimization and its application to fault diagnosis of rolling bearing[J]. Measurement, 2017, 103:321-332.
[6] Zheng J, Pan H, Cheng J. 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.
[7] Zhou J, Guo H, Zhang L, et al. Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD[J]. Shock and Vibration, 2016(6):1-10.
[8] Zhu K. Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance[J]. Journal of Vibration & Control, 2017(6):107-114.
[9] Yu H, Li H, Xu B. Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy[J]. Journal of Failure Analysis & Prevention, 2016, 16(4):655-666.
[10] Aouabdi S, Taibi M, Bouras S, et al. Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis[J]. Mechanical Systems & Signal Processing, 2017, 90:298-316.
[11] Li Y, Xu M, Wei Y, et al. A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree[J]. Measurement, 2016, 77:80-94.
[12] 李锦,宁新宝. 短时心率变异性信号的基本尺度熵分析[J]. 科学通报,2005,(14):1438-1441.
LI Jin, NING Xinbao. Basic Scale Entropy Analysis of Short - term Heart Rate Variability Signals[J]. Chinese Science Bulletin, 2005,(14):1438-1441.
[13] 黄晓林,崔胜忠,宁新宝等. 心率变异性基本尺度熵的多尺度化研究[J]. 物理学报,2009,58(12):8160-8165.
HUANG Xiaolin, CUI Shengzhong, NING Xinbao. Multiscale base-scale entropy analysis of heart rate variability[J]. Acta Phys. Sin, 2009,58(12):8160-8165.
[14] 刘大钊,王俊,李锦等. 颠倒睡眠状态调制心率变异性信号的功率谱和基本尺度熵分析[J]. 物理学报,2014,63(19):434-440.
LIU Dazhao, WANG Jun, LI Jin, et al. Analysis on power spectrum and base-scale entropy for heart rate variability signals modulated by reversed[J]. Acta Phys. Sin, 2014,63(19): 434-440.
[15] 刘铁兵,姚文坡,宁新宝等. 功能磁共振成像的基本尺度熵分析[J]. 物理学报,2013,62(21):501-506.
LIU Tiebing, YAO Wenpo, NING Xinbao,et al. The base scale entropy analysis of fMRI[J]. Acta Phys. Sin,2013, 62(21): 501-506.
[16] 钟先友,赵春华,陈保家等. 基于改进的本征时间尺度分解和基本尺度熵的齿轮故障诊断方法[J]. 中南大学学报(自然科学版),2015,46(03):870-877.
ZHONG Xianyou, ZHAO Chunhua, CHEN Baojia, et al. Gear fault diagnosis method based on IITD and base-scale entropy[J]. Journal of Central South University (Science and Technology), 2015,46(3):870-877.
[17] 许凡,方彦军,张荣等. 基于LMD基本尺度熵的AP聚类滚动轴承故障诊断[J]. 计算机应用研究,2017,34(06):1732-1736.
XU Fan, FANG Yan-jun, ZHANG Rong,et al. Roller bearings faults diagnosis method based on LMD，base-scale entropy and AP clustering[J]. Application Research of Computers,2017,34(6):1732-1736.
[18] Wang B, Hu X, Li H. Rolling Bearing Performance Degradation Condition Recognition Based on Mathematical Morphological Fractal Dimension and Fuzzy C-Means[J]. Measurement, 2017,109:1-8.
[19] Rai A, Upadhyay S H. Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering[J]. Mechanical Systems & Signal Processing, 2017, 93:16-29.
[20] 黄友朋,赵山,许凡,方彦军. EEMD排列熵与PCA-GK的滚动轴承聚类故障诊断[J]. 河南科技大学学报(自然科学版),2017,(02):17-24,30.
HUANG Youpeng, ZHAO Shan, FANG Yanjun. Rolling Bearing Clustering Faults Diagnosis Based on EEMD，Permutation Entropy and PCA-GK[J]. Journal of Henan University of Science and Technology(Natural Science), 2017,(02):17-24,30.