故障预测和系统健康管理的贝叶斯推断

项目名称： 故障预测和系统健康管理的贝叶斯推断

项目编号： No.11471275

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 数理科学和化学

项目作者： 徐国良

作者单位： 香港城市大学深圳研究院

项目金额： 60万元

中文摘要： 故障预测和系统健康管理（PHM）是一项现代的可靠性研究，旨在提升部件和系统的安全性和性能，因此它在各个领域引起了大众日益浓厚的兴趣。PHM 是一套系统性故障防止方法，它通过监视产品和系统的健康来防止故障的发生。故障预测可对系统即将发生的故障进行预警，从而在故障出现之前做出预防作业，以延长系统寿命。在建立了健康监视应用时，在退化数据的基础上采用基于模型的评估方法对剩余使用寿命进行估计。本项目涉及针对PHM 的剩余使用寿命进行统计建模和分析。由于高可靠性元件和系统模型上的复杂性，传统方法包括的最大似然估计可能无法或难以对剩余使用寿命做出推断。贝叶斯方法以其在计算和方法上的优势，已成为分析可靠性数据的一项替代方法。此外，与数据结合的先验信息可给出比仅采用数据更为精确的估计。本项目的主要目的在于在PHM中对传统方法难以实现的复杂模型进行贝叶斯推断

中文关键词： 故障预测；系统健康管理；贝叶斯推断

英文摘要： Prognostics and system health management (PHM) is a modern reliability study to improve the safety and performance of components and systems, and thus there has been a growing interest in a variety of fields including electronics, smart grid, nuclear plant, power industry, aerospace and military application, fleet industrial maintenance, and public health management. PHM is a systematic approach for failure prevention by monitoring the health/status of products and systems, predicting failure progression, and mitigating operating risks through repair or replacement. Prognostics can yield an advance warning of impending failure in a system, thereby helping in making maintenance decisions and executing preventive actions prior to failure occurrence to extend system life. Remaining useful life estimation, defined as the length from current time to the end of the useful life, is one of the vital indexes in PHM, and has been commonly used in reliability studies with applications. While the application of health/status monitoring is established, degradation data that describes quality characteristics over time is collected and model-based assessments based on degradation data are commonly used for remaining useful life estimation. Therefore, reliability assessment has been one of the important research topics in PHM, and there are many unsolved problems that are statistically challenging and important in engineering viewpoint. The project is concerned with some fundamental issues of statistical analysis for remaining useful life estimation for PHM. Due to sophisticated modeling of high reliable components and systems, classical approach may fail or become difficult to make inference on remaining useful life. Bayesian approach has become an alternative for analyzing the reliability data because of its computational and methodological advantages. Moreover, the prior information combined with the data produces more precise estimates than the data alone. Therefore, the applicability of Bayesian methods in reliability study has increased in recent years. The main objective of this project is to apply the Bayesian inference in complicated models in PHM where classical approaches are difficult to implicate.

英文关键词： prognostics;system health management;bayesian approach