基于性能退化的机械设备寿命预测  

Failure time estimation for mechanical device based on performance degradation

作　　者：王远航[1,2] 邓超[3] 胡湘洪 高军 黄创绵[1,2] 

机构地区：[1]工业和信息化部电子第五研究所,广东广州510610 [2]广州市电子信息产品可靠性与环境工程重点实验室,广东广州510610 [3]华中科技大学机械学院制造装备数字化国家工程中心,湖北武汉430074 [4]广东省电子信息产品可靠性技术重点实验室,广东广州510610 [5]广州韵脉质量技术服务有限公司,广东广州510610

出　　处：《计算机集成制造系统》2015年第8期2147-2157,共11页Computer Integrated Manufacturing Systems

基　　金：高端装备关键产品质量与可靠性公共技术服务平台建设资助项目(粤经信创新[2014]60);国家973计划资助项目(2011CB706803);国家自然科学基金资助项目(51375181;51475189)~~

摘　　要：为解决小样本机械设备的故障预测难题,提出一种基于性能退化的机械设备寿命预测方法。考虑机械设备退化过程受工况影响,提出局部退化规律的概念,并利用指数回归进行拟合表征,从而获得所有潜在的退化规律。通过表征局部退化规律的指数函数,计算局部寿命样本,进而获得各个时刻的局部寿命观测。鉴于机械设备在寿命周期的极限工况对应的剧烈退化少、中等工况对应的中等退化多,假设局部寿命来自某正态分布,并利用参数经验贝叶斯方法实现先验分布和后验分布的估计,全局寿命预测为最新观测对应的后验分布。通过仿真案例和数控机床的精度退化案例,验证了该方法具有良好的准确性和有效性。To solve the failure prediction problem in small sample machincal equipment, the issue of degradation- based failure time estimation for mechanical device was proposed. Since the degradation process was greatly affected by working conditions, the concept of local degradation rule was proposed and represented by the exponential func- tion, and all the potential degradation patterns could be obtained consequently. The fitted exponential curves were used to produce samples of local failure time, and the local failure time observations were obtained at each time. During the lifetime of mechanical device, the extreme degradation patterns were rare while the moderate ones were ＇ frequent, thus the local failure times were assumed to be independently from a normal prior distribution. Parametricempirical Bayes technique was adopted to estimate both the prior and posterior distribution. The posterior distribu- tion of last observation was treated as the global failure time estimation. The proposed approach was validated in a simulation case and a precision case of numerical control machine tool to show the better effectiveness.

关 键 词：机械设备 性能退化 寿命预测 指数函数 参数经验贝叶斯 

分 类 号：TH17[机械工程—机械制造及自动化] TP806.3[自动化与计算机技术—检测技术与自动化装置]