Assessment Method of Rolling Bearing Performance Degradation based on Wavelet Packet Singular Spectral Entropy and SVDD
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摘要: 针对设备的视情维修,提出一种将小波包奇异谱熵和支持向量数据描述(SVDD)相结合的滚动轴承性能退化评估方法。先提取轴承全寿命周期内振动信号的小波包奇异谱熵作为轴承状态的特征矢量,然后以轴承正常状态下的特征矢量训练SVDD,得到正常状态下的基准超球体,再计算轴承全寿命周期内的特征矢量与基准超球体之间的相对距离,作为性能退化过程的定量评估指标,并对失效阈值和早期故障阈值进行设定。结果表明,与基于小波包和SVDD的性能退化评估方法相比,该方法的早期故障检测能力更强,对轴承性能退化各个阶段的描述更加准确。最后,利用基于EMD的Hilbert包络解调方法对评估结果的正确性进行了验证。Abstract: Aiming at the condition-based maintenance of equipments, a novel assessment method of rolling bearing performance degradation combining wavelet packet singular spectral entropy (WPSSE) and support vector data description (SVDD) was proposed. Firstly, WPSSEs were extracted from bearing full-life-cycle vibration signals as feature vectors to describe a bearing running state. Secondly, SVDD was trained using the feature vectors under normal condition to get the fiducial hypersphere of normal state. Then, the relative distance between full-life-cycle feature vectors of bearing and fiducial hypersphere was calculated as a quantitative index of performance degradation, and the failure threshold and incipient fault threshold were set. Analytical results of experimental data indicated that compared with the degradation evaluation method based on wavelet packet and SVDD, the proposed method had stronger ability for incipient fault detection, and it could describe the stages of bearing performance degradation more accurately. Finally, Hilbert envelope demodulation method based on empirical mode demodulation was used to validate the reliability of evaluation result.
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Key words:
- rolling bearing /
- feature extraction /
- fault detection /
- wavelet packet singular spectral entropy /
- SVDD
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[1] 张龙,张磊,熊国良,等.基于多尺度熵的滚动轴承Elman神经网络故障诊断方法[J].机械科学与技术,2014,33(12):1854-1858 Zhang L, Zhang L, Xiong G L, et al. Rolling bearing fault diagnosis based on multiscale entropy and Elman neural network[J]. Mechanical Science and Technology for Aerospace Engineering, 2014,33(12):1854-1858 (in Chinese) [2] Dong S J, Luo T H. Bearing degradation process prediction based on the PCA and optimized LS-SVM model[J]. Measurement, 2013,46(9):3143-3152 [3] 肖文斌,陈进,周宇,等.小波包变换和隐马尔可夫模型在轴承性能退化评估中的应用[J].振动与冲击,2011,30(8):32-35 Xiao W B, Chen J, Zhou Y, et al. Wavelet packet transform and hidden Markov model based bearing performance degradation assessment[J]. Journal of Vibration and Shock, 2011,30(8):32-35 (in Chinese) [4] 张龙,黄文艺,熊国良,等.基于TESPAR与GMM的滚动轴承性能退化评估[J].仪器仪表学报,2014,35(8):1772-1779 Zhang L, Huang W Y, Xiong G L, et al. Bearing performance degradation assessment based on TESPAR and GMM[J]. Chinese Journal of Scientific Instrument, 2014,35(8):1772-1779 (in Chinese) [5] Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004,54(1):45-66. [6] 李凌均,韩捷,郝伟,等.支持向量数据描述用于机械设备状态评估研究[J].机械科学与技术,2005,24(12):1426-1429 Li L J, Han J, Hao W, et al. Condition evaluation for mechanical equipment by means of support vector data description[J]. Mechanical Science and Technology, 2005,24(12):1426-1429 (in Chinese) [7] 潘玉娜,陈进.结合循环平稳和支持向量数据描述的轴承性能退化评估研究[J].机械科学与技术,2009,28(4):442-445 Pan Y N, Chen J. Assessment of bearing performance degradation by cyclostationarity analysis and support vector data description[J]. Mechanical Science and Technology for Aerospace Engineering, 2009,28(4):442-445 (in Chinese) [8] Zhu X R, Zhang Y Y, Zhu Y S. Bearing performance degradation assessment based on the rough support vector data description[J]. Mechanical Systems and Signal Processing, 2013,34(1-2):203-217 [9] 潘玉娜,陈进,李兴林.奇异谱熵在滚动轴承性能退化评估中的应用研究[J].振动与冲击,2012,31(S):107-109 Pan Y N, Chen J, Li X L. Singular spectral entropy applied to rolling bearing performance degradation assessment[J]. Journal of Vibration and Shock, 2012,31(S):107-109 (in Chinese) [10] 王玉梅,董洋洋,刘兴艳.高阶小波包奇异谱熵在故障选线中的应用研究[J].电力系统保护与控制,2011,39(8):23-27 Wang Y M, Dong Y Y, Liu X Y. Study on higher order wavelet packet singular entropy and its application to faulty line selection[J]. Power System Protection and Control, 2011,39(8):23-27 (in Chinese) [11] Nikolaou N G, Antoniadis I A. Rolling element bearing fault diagnosis using wavelet packets[J]. NDT & E International, 2002,35(3):197-205 [12] 谭善文,秦树人,汤宝平.小波基时频特性及其在分析突变信号中的应用[J].重庆大学学报(自然科学版),2001,24(2):12-17 Tan S W, Qin S R, Tang B P. Time-frequency characteristic of wavelet base and its application transient signal detection[J]. Journal of Chongqing University (Natural Science Edition), 2001,24(2):12-17 (in Chinese) [13] 张遂强,郝伟,李志农.基于全信息技术的自适应报警方法研究[J].机械科学与技术,2006,25(12):1499-1502 Zhang S Q, Hao W, Li Z N. Study of an adaptive alarm method based on full information technique[J]. Mechanical Science and Technology, 2006,25(12):1499-1502 (in Chinese) [14] Qiu H, Lee J, Lin J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006,289(4-5):1066-1090 [15] 陈斌,阎兆立,程晓斌.基于SVDD和相对距离的设备故障程度预测[J].仪器仪表学报,2011,32(7):1558-1563 Chen B, Yan Z L, Cheng X B. Machinery fault trend prediction based on SVDD and relative distance[J]. Chinese Journal of Scientific Instrument, 2011,32(7):1558-1563 (in Chinese)
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