综合退化数据与寿命数据的某型电连接器寿命预测方法

上海交通大学学报（自然版） ›› 2014, Vol. 48 ›› Issue (05): 702-706.

综合退化数据与寿命数据的某型电连接器寿命预测方法

王浩伟a,徐廷学a,周伟b

（海军航空工程学院 a. 兵器科学与技术系，山东烟台 264001；b. 青岛校区，山东青岛 266000）

收稿日期:2013-06-13
基金资助:
国家自然科学基金资助项目（61273058）

Lifetime Prediction Method for Missile Electrical Connector Synthesizing Degradation Data and Lifetime Data

WANG Haoweia，XU Tingxuea，ZHOU Weib

(a. Department of Ordnance Science and Technology, Yantai 264001, Shandong, China; b. Qingdao Campus, Naval Aeronautical and Astronautical University, Qingdao 266000, Shandong, China)

Received:2013-06-13

摘要/Abstract

摘要：

针对基于寿命数据的预测方法效率不高的问题，在进行温、湿度双应力加速退化试验的基础上，研究了通过分析加速退化数据而进行寿命预测的途径.为了充分利用所收集的产品寿命数据来提高预测精度，提出了综合退化数据与寿命数据的寿命预测方法.分别使用Wiener过程、逆高斯分布和广义Eyring模型对产品性能的退化数据、少量寿命数据以及产品反应率进行建模，并建立了综合以上信息的可靠性模型.在此基础上，利用Markov Chain Monte Carlo仿真方法得到模型参数的点估计值，通过Bootstrap自助抽样法获得了模型参数的估计区间.结果表明，所提出的综合退化数据与寿命数据的方法可以提高寿命预测精度.

关键词: 电连接器, 寿命预测, 双应力加速退化试验, Wiener过程

Abstract:

To improve the prediction accuracy of the lifetime of a certain type of missile connector, a prediction method synthesizing degradation data and lifetime data was proposed. By modeling the degradation data with Wiener process, modeling a small amount of lifetime data with inverse Gaussian distribution, establishing the reaction rate model with the generalized Eyring model, a comprehensive reliability model was established. Furthermore, the point estimates of the model parameters were obtained through the Markov Chain Monte Carlo simulation method, and the interval estimates were obtained through a bootstrap method. By comparing the prediction results, it was concluded that the approach synthesizing degradation data and lifetime data could improve the prediction accuracy.

Key words：

Key words: electrical connector, lifetime prediction, double-stress accelerated degradation test, Wiener process

中图分类号:

王浩伟a,徐廷学a,周伟b. 综合退化数据与寿命数据的某型电连接器寿命预测方法[J]. 上海交通大学学报（自然版）, 2014, 48(05): 702-706.

WANG Haoweia，XU Tingxuea，ZHOU Weib. Lifetime Prediction Method for Missile Electrical Connector Synthesizing Degradation Data and Lifetime Data[J]. Journal of Shanghai Jiaotong University, 2014, 48(05): 702-706.

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