Bayesian-based information extraction and aggregation approach for multilevel systems with multi-source data

作     者：Lechang Yang Jianguo Zhang Yanling Guo Qian Wang 

作者机构：School of Reliability and Systems Engineering Beihang University Beijing 100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beihang University Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2017年第28卷第2期

页      面：385-400页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：supported by the National Basic Research Program of China(973 Program)(2013CB733000) 

主　　题：Artificial intelligence Data fusion Information analysis Information retrieval Reliability Reliability analysis 

摘      要：The ever-increasing complexity of industry facilities has made the reliability analysis and assessment an imperative yet tough work. Motivated by practical engineering requirement, this paper develops a Bayesian-based information extraction and aggregation (BIEA) approach for system level reliability estimation of a complex system. It takes both subjective judgments and objective field outputs into consideration. Novel features of this approach is a unique information content based aggregation process, which allows a flexible application of this framework in separated modules on account for purpose. The coherency of which is guaranteed by the objective information content calculation. This work goes beyond the alternatives that deal with solely attributed data under ideal information circumstance, and investigates a more generic tool for real engineering application. Limitations embedded in traditional statistical modeling methods have been eliminated in a nature manner by information transition and integration. In addition, a double axis driving mechanism (DADM) for erecting the antenna of a satellite is demonstrated as case study for benefit illustration and effectiveness verification. © 2017 Beijing Institute of Aerospace Information.