知识与数据融合的可靠性定量模型建模方法

郝志鹏; 曾声奎; 郭健彬; 马纪明; 李齐林

doi:10.13700/j.bh.1001-5965.2015.0029

知识与数据融合的可靠性定量模型建模方法

doi: 10.13700/j.bh.1001-5965.2015.0029

郝志鹏¹,
曾声奎^1,2, ,,
郭健彬^1,2,
马纪明³,
李齐林⁴

1.
北京航空航天大学可靠性与系统工程学院, 北京 100083
2. 北京航空航天大学可靠性与环境技术国防重点实验室, 北京 100083
3. 北京航空航天大学中法工程师学院, 北京 100083
4. 中航工业金城南京机电液压工程研究中心, 南京 211140

基金项目: 国家自然科学基金(61304218)

详细信息

作者简介:
郝志鹏男,博士研究生。主要研究方向:可靠性与性能一体化设计、可靠性评估等。Tel.:010-82338403E-mail:haozhipeng@buaa.edu.cn;曾声奎男,博士,教授。主要研究方向:可靠性综合设计与仿真、可靠性与性能一体化设计、人机系统可靠性分析、计算机辅助可靠性工程等。Tel.:010-82314731E-mail:zengshengkui@buaa.edu.cn;郭健彬男,博士,讲师。主要研究方向:系统可靠性设计分析、机电产品可靠性仿真、RMS综合集成平台、多学科设计优化。Tel.:010-82333839E-mail:guojianbin@buaa.edu.cn;马纪明男,博士,讲师。主要研究方向:机电系统性能与可靠性一体化设计方法、机电系统多学科设计优化方法、控制系统可靠性分析方法、随机作用下的系统响应特性分析。Tel.:010-82339761E-mail:jiming.ma@buaa.edu.cn;李齐林男,硕士,工程师。主要研究方向:机电系统性能与可靠性一体化设计方法、机电系统多学科设计优化方法。

通讯作者:
曾声奎,Tel.:010-82314731E-mail:zengshengkui@buaa.edu.cn

中图分类号: V24;TB114.3
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出版历程
- 收稿日期: 2015-01-15
- 网络出版日期: 2016-01-20

Integrated method of knowledge and data for quantitative reliability modeling

1.
School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2. Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
3. Sino-French Engineer School, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
4. Jincheng Nanjing Electrical and Hydraulic Engineering Research Center, AVIC, Nanjing 211140, China

Funds: National Natural Science Foundation of China(61304218)

摘要

摘要: 可靠性定量设计的关键是建立可靠性定量模型。现有的可靠性定量模型建模方法主要基于设计人员对产品对象故障规律的知识,包括故障模式、环境扰动、故障机理等。但知识固有的有限性和不完整性必然会给可靠性定量模型带来模型误差和输入参数的不确定性。针对这个问题,提出了基于贝叶斯理论融合知识和数据的可靠性定量模型建模方法,量化并更新模型误差和输入参数的不确定性。为此,首先说明了知识与数据融合的可靠性定量模型建模工作,建立了知识与数据融合的可靠性定量模型建模框架;接着阐述了基于贝叶斯理论的知识与数据融合原理;然后介绍了基于贝叶斯理论融合知识与数据的通用方法,并分别针对性能波动数据和性能退化数据2种常见数据类型进一步详细讨论了各自适用的贝叶斯融合方法;最后通过机载轴向柱塞泵的案例验证了前述方法的可行性和有效性。
- 可靠性定量设计 /
- 可靠性定量模型 /
- 贝叶斯融合 /
- 知识 /
- 数据
Abstract: Keys of quantitative reliability design lie in the establishment of the quantitative reliability model. Current modeling methods mainly rely on design staff's knowledge on product failure rules, including failure modes, environmental disturbances, failure mechanisms, etc. However, the inherent finiteness and imperfection of knowledge are bound to bring both model error and input uncertainties to the quantitative reliability model. To address this problem, we proposed a knowledge-and-data integrated Bayesian modeling method to develop the quantitative reliability model, quantifying the model error and input uncertainties. First of all, the tasks of the knowledge-and-data integrated modeling of the quantitative reliability model were explained, and the corresponding framework was established. Then the principle of Bayesian integration of knowledge and data was clarified. After that, the general method of Bayesian integration of knowledge and data was proposed, and two specific Bayesian integration methods for both performance fluctuation and degradation data were addressed respectively. Finally, the effectiveness and feasibility of the proposed method were illustrated by a case of an airborne axial piston pump.
- quantitative reliability design /
- quantitative reliability model /
- Bayesian integration /
- knowledge /
- data

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