Due to lack of the failure data, the traditional statistics-based reliability evaluation method cannot effectively evaluate the reliability of the mechanical equipment. The operational reliability concept was introduced in the present paper to describe the running condition of the equipment, and a prediction method for the operational reliability based on relevance vector machine(RVM) was proposed. In the method, the operational reliability calculation model was adopted firstly to establish the relationship between status indicators of equipment and the operational reliability, and then the relevance vector machine was introduced to predict the trend and the corresponding probability distribution function(PDF) of the status indicators. Finally, the predicted values and PDF of status indicators were inputted into the operational reliability calculation model and the predicted operational reliability of the equipment was then obtained. The method was applied to the operational reliability evaluation of aero engine bearings, the result showed that the error between the predicted failure time and the actual failure time was within 5%, therefore the rationality and effectiveness of the method were proved.
冯鹏飞，朱永生，王培功，闫柯. 基于相关向量机模型的设备运行可靠性预测[J]. 振动与冲击, 2017, 36(12): 146-149.
Feng Peng-fei, Zhu Yong-sheng, Wang Pei-gong, Yan Ke. Operational Reliability Prediction of Equipment Based on Relevance Vector Machine. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(12): 146-149.
[1] 额田启三.机械可靠性与故障分析[M].北京：国防工业出版社，2006:6-7.
[2] Gorjian N, Ma L, Mittinty M, et al. A review on degradation models in reliability analysis[M]// Engineering Asset Lifecycle Management. Springer London, 2010:369-384.
[3] 方明杰, 周光辉, 程元森,等. 基于运行状态信息的数控珩磨机液压系统可靠性预测方法[J]. 应用科技, 2012,39:30-33.
Fang Ming-jie, Zhou Guang-hui, Cheng Yuan-sen, et al. Reliability prediction for hydraulic system of CNC honing machine based on the operational status information[J]. Applied Science and Technology,2012,39:30-33
[4] Lu H, Kolarik W J, Lu H. Real-time performance reliability prediction[J]. IEEE Transactions on Reliability, 2002, 50(4):353-357.
[5] 陈保家, 陈雪峰, 何正嘉,等. 利用运行状态信息的机床刀具可靠性预测方法[J]. 西安交通大學學報, 2010, 44(9):74-77.
Chen Bao-jia, Chen Xue-feng, He Zheng-jia, et al. Operating Condition Information—Based Reliability Prediction of Cutting Tool[J]. Journal of Xi'an Jiaotong University,2010,44(9):74-77.
[6] Tipping M E. Sparse Bayesian Learning and the Relevance Vector Machine[J]. Journal of Machine Learning Research, 2001, 1(3):211-244.
[7] 曲晓燕, 范庚, 马登武. 基于相关向量机的导弹贮存可靠性预测方法研究[J]. 计算机测量与控制, 2013, 21(11):3021-3022.
Qu Xiao-yan, Fan Geng, Ma Deng-wu. Study on Storage Reliability Prediction of Missiles Based on Relevance Vector Machine[J]. Computer Measurement & Control, 2013, 21(11):3021-3022.
[8] 赵金晶, 李松娜. 基于相关向量机的股指数预测及其置信区间[J]. 四川教育学报, 2007, 23:104-106. DOI:10.3969/j.issn.1000-5757.2007.11.037.
Zhao Jin-jing, Li Song-nan. The Relevance Vector Machine Based Stock Market Index Forecasting and Confidence Interval[J]. Journal of Sichuan College of Education,2007,23:104-106. DOI:10.3969/j.issn.1000-5757.2007.11.037.
[9] Heng A, Zhang S, Tan A C C, et al. Rotating machinery prognostics: State of the art, challenges and opportunities[J]. Mechanical Systems & Signal Processing, 2009, 23(3):724-739.
[10] Aiwina Heng, Andy C.C. Tan, Joseph Mathew, et al. Intelligent condition-based prediction of machinery reliability[J].Mechanical Systems\s&\signal processing, 2009, 23(5):1600–1614.
[11] 陈玉. 基于状态信息的机械设备运行可靠性评估方法研究[D].西安交通大学，2014
[12] 陈保家. 基于设备运行状态的可靠性评估技术及应用研究[D].西安交通大学，2011
