基于混合不确定性量化和高置信度数值模拟的复杂机械系统健康评估

Health assessment, including a comprehensive assessment of system state, damage assessment, and reliability assessment, is a primary work ensuring safety, dependability and effectiveness of complex mechanical systems, as that design decision-making must rely on the results of high-confidence numerical simulation, in which uncertainty quantification play a critical role. In consideration that reliability analysis and health assessment of complex systems are often faced with inaccurate and incomplete information, along with excessive calculation in the process of non-probabilistic reliability assessment and optimization, this project investigates an application with such typical complex mechanical systems, by means of imprecise probability theory, info-gap theory, and quantification of margins and uncertainty, with the aim of such key scientific issues as follows: an integrated quantification approach dealing with aleatory and epistemic uncertainty has been put forward, integrating multi-source information with test data, results of numerical simulation and expert knowledge; an assessment framework of high-efficiency surrogate model can be established for high-confidence numerical simulation under hybrid uncertainty. Using modeling, simulation, model verification and validation, it may lead to a significance of enhancing reliabilty, especially effectiveness of complex systems, and bring practical value for improving the quality of the product design.

对复杂机械系统进行包括系统状态综合评估、损伤评估和可靠度评估在内的健康评估是保证其安全、可靠和有效的基础，相关的决策必须以高置信度数值模拟结论为依据，而不确定性量化是验证和评估置信度问题的核心和难点。本项目针对复杂系统可靠性分析和健康评估中常常面临的信息不精确和不完备情况，以及非概率可靠性评估和优化过程中计算量过大的问题，以某一个典型复杂机械系统为应用对象，将裕量与不确定性量化(QMU)方法与非精确概率论、信息差理论等非概率模型，以及模型验证和确认方式相结合，研究可以综合处理随机不确定性和认知不确定性两类不确定性、能够融合试验数据/数值模拟结果/专家知识等多源信息的不确定性综合量化方法，以及具有较高效率的进行混合不确定条件下高置信度模拟的模型验证与健康评估体系。该项目对复杂系统的可靠性评估特别是有效性评估具有重要意义，对于提高产品设计质量具有实际价值。

本项目针对复杂系统可靠性分析和健康评估中常常面临的信息不精确和不完备情况，以及非概率可靠性评估和优化过程中计算量过大的问题，以多类典型复杂机械系统为应用对象，进行包括系统状态综合评估、损伤评估和可靠度评估在内的健康评估，用于提高产品的安全性、可靠性和有效性，同时解决高置信度数值模拟过程中混合不确定性量化的难点。通过将裕量与不确定性量化(QMU)方法与非精确概率论、信息差理论等非概率模型，以及模型验证和确认方式相结合，项目提供了可以综合处理随机不确定性和认知不确定性两类不确定性、能够融合试验数据/数值模拟结果/专家知识等多源信息的不确定性综合量化方法，以及具有较高效率的进行混合不确定条件下高置信度模拟的模型验证与健康评估体系。该项目对复杂系统的可靠性评估特别是有效性评估具有重要意义，对于提高产品设计质量具有实际价值。

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