A Relevance Vector Machine Prediction Method Based on Adaptive Multi-kernel Combination and Its Application to Remaining Useful Life Prediction of Machinery

doi:10.3901/JME.2016.01.087

Abstract

Abstract: In view of some shortcomings of support vector machine, for instance, it is difficult to select the regularization parameter and the kernel function must satisfy Mercer’s condition, relevance vector machine (RVM) is developed and applied to the field of trend prediction. The performance of RVM, to a large extent, depends on the kernel function. However, a single kernel function is generally selected artificially and subjectively in current studies on RVM, which increases its dependency of the RVM to parameters and decreases the robustness in prediction process. To solve the problem, a new adaptive multi-kernel RVM is proposed for prediction. In the method, multiple kernel functions are selected originally and their weights are generated by the particle filter (PF) algorithm to construct multi-kernel RVM models. Then the optimal multi-kernel RVM model is obtained by iterative processes, i.e., predicting, weights updating and resampling. The effectiveness of the proposed method is validated by a simulation study and a case study of remaining useful life prediction of machinery. The results demonstrate that the proposed method obtains higher prediction accuracies compared with the single kernel RVM models.

Key words: machinery, multi-kernel relevance vector machine, remaining useful life prediction

CLC Number:

TH17

LEI Yaguo, CHEN Wu, LI Naipeng, LIN Jing. A Relevance Vector Machine Prediction Method Based on Adaptive Multi-kernel Combination and Its Application to Remaining Useful Life Prediction of Machinery[J]. Journal of Mechanical Engineering, 2016, 52(1): 87-93.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2016.01.087

http://www.cjmenet.com.cn/EN/Y2016/V52/I1/87

References

[1] 雷亚国，何正嘉，訾艳阳. 基于混合智能新模型的故障诊断[J]. 机械工程学报，2008，44(7)：112-117. LEI Yaguo，HE Zhengjia，ZI Yanyang. Fault diagnosis based on novel hybrid intelligent model[J]. Chinese Journal of Mechanical Engineering，2008，44(7)：112-117.
[2] SOUALHI A，MEDJAHER K，ZERHOUNI N. Bearing health monitoring based on Hilbert–Huang transform，support vector machine, and regression[J]. IEEE Transactions on Instrumentation and Measurement，2015，64(1)：52-62.
[3] NIZAM M，MOHAMED A，HUSSAIN A. Dynamic voltage collapse prediction in power systems using support vector regression[J]. Expert Systems with Applications，2010，37(5)：3730-3736.
[4] TRAN V T，THOM PHAM H，YANG B S，et al. Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine[J]. Mechanical Systems and Signal Processing，2012，32：320-330.
[5] 申中杰，陈雪峰，何正嘉，等. 基于相对特征和多变量支持向量机的滚动轴承剩余寿命预测[J]. 机械工程学报，2013，49(2)：183-189. SHEN Zhongjie，CHEN Xuefeng，HE Zhengjia，et al. Remaining life predictions of rolling bearing based on relative features and multivariable support vector machine[J]. Journal of Mechanical Engineering，2013，49(2)：183-189.
[6] TIPPING M E. The relevance vector machine[J]. Advances in Neural Information Processing Systems，2000，12：652-658.
[7] TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research，2001，1：211-244.
[8] CAESARENDRA W，WIDODO A，YANG B S. Application of relevance vector machine and logistic regression for machine degradation assessment[J]. Mechanical Systems and Signal Processing，2010，24(4)：1161-1171.
[9] WIDODO A，YANG B S. Application of relevance vector machine and survival probability to machine degradation assessment[J]. Expert Systems with Applications，2011，38(3)：2592-2599.
[10] 段青，赵建国，马艳. 优化组合核函数相关向量机电力负荷预测模型[J]. 电机与控制学报，2010，14(6)：33-38. DUAN Qing，ZHAO Jianguo，MA Yan. Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting[J]. Electric Machines and Control，2010，14(6)：33-38.
[11] 朱永利，尹金良. 组合核相关向量机在电力变压器故障诊断中的应用研究[J]. 中国电机工程学报，2013，33(22)：68-74. ZHU Yongli，YIN Jinliang. Study on application of multi-kernel learning relevance vector machines in fault diagnosis of power transformers[J]. Proceedings of the CSEE，2013，33(22)：68-74.
[12] DOUCET A，DE FREITAS N，GORDON N. Sequential Monte Carlo methods in practice[M]. New York：Springer，2001.
[13] SMITS G F，JORDAAN E M. Improved SVM regression using mixtures of kernels[C]//IEEE International Joint Conference on Neural Networks，May 12-17，2002，Honolulu，HI. IEEE，2002，3：2785-2790.
[14] MüLLER K R，SMOLA A J，R?TSCH G，et al. Predicting time series with support vector machines[M]. Berlin Heidelberg：Springer，1997.
[15] ECTOUX P，GOURIVEAU R，MEDJAHER K，et al. PRONOSTIA：An experimental platform for bearings accelerated degradation tests[C]//IEEE International Conference on Prognostics and Health Management，June 18-21，2012，Denver，Colorado，United States. IEEE，2012：1-8.
[16] The IEEE Reliability Society. IEEE PHM 2012 prognostic challenge. outline，experiments，scoring of results，winners[EB/OL]. [2015-03-20]. http：//www.femto-st.fr/f/d/IEEEPHM2012-challenge-details. pdf.
[17] LI Naipeng，LEI Yaguo，LIU Zongyao，et al. A particle filtering-based approach for remaining useful life predication of rolling element bearings[C]//IEEE International Conference on Prognostics and Health Management，June 22-25，2014，Cheney，Washington. IEEE，2014：1-8.