计算机工程与应用2016，Vol.52Issue(23)：249-254,6.DOI:10.3778/j.issn.1002-8331.1411-0047
退化数据驱动的设备剩余寿命在线预测
Degradation data driven online prediction for equipment residual life
摘要
Abstract
To predict available residual life of single service equipment, a prediction method of residual life which com-bines prior and current degradation data is proposed. The equipment degradation model is constructed, conforming to a nonlinear Wiener process. The unknown parameters are estimated by using the Maximum Likelihood Estimate(MLE) method. Parameters are updated by using the Bayesian method when new degradation data is available. After that, the real-time residual life is further evaluated. Numerical simulation and case study are conducted. Results indicate that the presented method can update the evaluation distribution of residual life by using new degradation data, reflect differences between individual equipment well and significantly reduce uncertainty of the residual life distribution, compared with the fixed parameter method.关键词
剩余寿命/非线性维纳过程/贝叶斯方法/退化/极大似然法Key words
residual life/nonlinear Wiener process/Bayesian method/degradation/Maximum Likelihood Estimate(MLE)分类
信息技术与安全科学引用本文复制引用
史华洁,薛颂东..退化数据驱动的设备剩余寿命在线预测[J].计算机工程与应用,2016,52(23):249-254,6.基金项目
国家自然科学基金（No.61472269，No.61403271）；山西省自然科学基金（No.2014011019-2）；山西省科技攻关计划项目（No.2015031004）；山西省回国留学人员科研资助项目（No.2016-091）。 （）
