基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测&nbsp;

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (9) : 149-153.

论文

基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测

肖婷，汤宝平，秦毅，陈昌

作者信息 +

Degradation Trend Prediction of Rolling Bearing Based on Manifold Learning and Least Squares Support Vector Machine

XIAO Ting, TANG Bao-ping*，QIN Yi，CHEN Chang

Author information +

文章历史 +

摘要

为更好地表征滚动轴承性能退化趋势，提出基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测新方法。提取振动信号的多域特征组成高维特征集，利用局部保持投影算法(LPP)对多域高维特征集进行维数约简，消除各特征指标之间的冗余、冲突等问题。将维数约简后的特征向量作为最小二乘支持向量机的输入，建立退化趋势预测模型，完成退化趋势预测。运用实测的滚动轴承全寿命实验数据进行检验，结果表明本方法能获得准确的预测结果。

Abstract

A new prediction method is proposed based on manifold learning and least squares support vector machine to describe the rolling bearing degradation trend. Time-domain features and features based on information entropy were extracted to construct high-dimensional characteristic sets. The locality preserving projection algorithm was used for dimensionality reduction in order to eliminate the problem of redundancy between each indicators. The characteristic features were input to the least squares support vector machine to train and construct a model, so as to accomplish the trend prediction. The rolling bearing run-to-failure tests are carried out to inspect the prediction model, and the results demonstrate the effectiveness and accurateness of the proposed method.

导出引用

肖婷，汤宝平，秦毅，陈昌. 基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测 [J]. 振动与冲击, 2015, 34(9): 149-153

XIAO Ting, TANG Bao-ping*，QIN Yi，CHEN Chang. Degradation Trend Prediction of Rolling Bearing Based on Manifold Learning and Least Squares Support Vector Machine[J]. Journal of Vibration and Shock, 2015, 34(9): 149-153

参考文献

[1] Gebraeel N，Lawley M，Liu R，et al. Residual life predictions from vibration-based degradation signals: a neural network approach [J]. Industrial Electronics, IEEE Transactions on, 2004, 51(3): 694-700.
[2] S. Janjarasjitt, H. Ocak, K.A. Loparo. Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal [J]. Journal of Sound and Vibration, 317 (2008): 112–126．
[3] 奚立峰，黄润青，李兴林，等. 基于神经网络的球轴承剩余寿命预测[J]. 机械工程学报，2007，43(10): 137-143.
XI Li-feng, HUANG Run-qing, LI Xing-lin, etal. Residual life predictions for ball bearing based on neural networks [J]. Chinese Journal of Mechanical Engineering, 2007，43(10): 137-143.
[4] Antoni J. Cyclic spectral analysis of rolling-element bearing signals: facts and fictions [J]. Journal of Sound and Vibration, 2007, 304(3-5): 497-529.
[5] 王小玲，陈进，从飞云. 基于时频的频带熵方法在滚动轴承故障识别中的应用[J]. 振动与冲击，2012，31(18): 29-33.
WANG Xiao-ling, CHEN Jin, CONG Fei-yun. Application of spectral band entropy (SBE) method in rolling bearing fault diagnosis based on time-frequency analysis [J]. Journal of Vibration and Shock, 2012, 31(18): 29-33.
[6] 郭磊，陈进. 小波包熵在设备性能退化评估中的应用[J]. 机械科学与技术，2008，27(9): 1203-1206.
GUO Lei, CHEN Jin. Application of Wavelet Packet Entropy to Equipment Performance Degradation Assessment [J]. Mechanical Science and Technology for Aerospace Engineering, 2008, 27(9): 1203-1206.
[7] 宋涛，汤宝平，李锋. 基于流形学习和 K-最近邻分类器的旋转机械故障诊断方法[J]. 振动与冲击，2013，32(5): 149-153.
SONG Tao, TANG Bao-ping, LI Feng. Fault diagnosis method for rotating machinery based on manifold learning and K-nearest neighbor classifier [J]. Journal of Vibration and Shock, 2013, 32(5): 149-153.
[8] Dong S, Luo T. Bearing degradation process prediction based on the PCA and optimized LS-SVM model [J]. Measurement, 2013, 46(9): 3143-3152.
[9] 董绍江，汤宝平，张焱. 基于非广延小波特征尺度熵和支持向量机的轴承状态识别[J]. 振动与冲击，2012，31(15): 50-54.
DONG Shao-jiang, TANG Bao-ping, ZHANG Yan. Bearing running state recognition based on non-extensive wavelet feature scale entropy and support vector machine [J]. Journal of Vibration and Shock, 2012，31(15): 50-54.
[10] Yan J, Guo C, Wang X. A dynamic multi-scale Markov model based methodology for remaining life prediction [J]. Mechanical Systems and Signal Processing, 2011, 25(4): 1364-1376.
[11] 石林锁，沈金伟，张亚洲，等. 基于AR模型和谱峭度法的滚动轴承故障诊断[J]. 振动与冲击，2011，30(12): 257-260.
SHI Lin-suo, SHEN Jin-wei, ZHANG Ya-zhou, etal. Fault diagnosis of a rolling element bearing based on AR model and spectral kurtosis [J]. Journal of Vibration and Shock, 2011, 30(12): 257-260.
[12] 田中大，高宪文，李琨. 基于KPCA与LSSVM的网络控制系统时延预测方法[J]. 系统工程与电子技术，2013，35(6):1281-1285.
TIAN Zhong-da, GAO Xian-wen, LI Kun. Networked control system time-delay prediction method based on KPCA and LSSVM [J]. Systems Engineering and Electronics, 2013, 35(6):1281-1285.
[13] 何强，蔡洪，韩壮志，等. 基于非线性流形学习的 ISAR 目标识别研究[J]. 电子学报，2010，38(003): 585-590.
HE Qiang, CAI Hong, HAN Zhuang-zhi, etal. ISAR Target Recognition Based on Nonlinear Manifold Learning [J]. Acta Electronica Sinica, 2010, 38(003): 585-590.
[14] X.F. He, P. Niyogi, Locality preserving projections, in: Proceedings of the Conference on Advances in Neural Information Processing Systems, 8–13 December 2003, Vancouver, Canada, MIT Press, Cambridge, MA, 2004, pp. 1–8.
[15] J. Lee, H. Qiu, G. Yu, J. Lin, Rexnord Technical Services, “Bearing Data Set” ,IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, <http://ti.arc.nasa.gov/project/prognostics-data-repository>, NASA Ames, Moffett Field, CA, 2007