权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Efficient reliability analysis of complex systems

复杂系统的高效可靠性分析

基本信息

批准号：
335796111
负责人：
Professor Dr.-Ing. Michael Beer
金额：
--
依托单位：
Institut für Risiko und Zuverlässigkeit
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/335796111?language=en
关键词：
Efficient reliability analysis complex systems

项目摘要

In this project a theoretical and algorithmic basis is developed in order to analyse the time dependent reliability of complex systems of industrial size with high numerical efficiency and close to reality. These fundamental developments are particularly important in order to cope with the rapidly growing complexity of our industrial systems and to ensure their reliability. Those systems, such as supply and sewage systems, industrial and production facilities, control systems in buildings or on construction sites or in vehicles, power plants or structural systems, are of increasing criticality for the functionality of our society and daily life. The planned project is devoted to securing the reliability of such systems. The envisaged development addresses the key discrepancy of the current methods and technologies; for systems of industrial size they require a critical compromise between very strong simplifications of the model and an infeasible numerical effort associated with a detailed modelling. This discrepancy is rapidly growing with the complexity of systems and, thus, causes growing uncertainties in the modelling, in addition.The concept of the survival signature forms the basis for the development. This concept facilitates a generally applicable, powerful, flexible and transparent modelling of a system. Starting from this concept the systematic evaluation of the system states is replaced by a statistical estimation realized through efficient stochastic simulation. As a consequence the quality of the results becomes independent of the dimensionality of the problem, i.e. independent of the complexity of the system. The numerical efficiency of the analysis of complex systems is increased by several magnitudes. On this basis the time dependent reliability analysis of complex systems can be analysed in a detailed and realistic manner and simultaneously with high numerical efficiency. Moreover, the sophisticated simulation approaches are used for a targeted identification of critical failure scenarios, system components and system domains. The following hypothesis is derived from the statistical approach as a fundamental finding. When analysing complex systems, an increase of complexity does not necessarily require the analysis of an increased number of system states to ensure a consistent quality of the prognoses regarding system behaviour. The validity of this hypothesis is verified and demonstrated in the project.The uncertainties and the lack of information in the modelling, which result from growing complexity, are captured quantitatively with the aid of a concept of imprecise probabilities and are taken into account in the reliability analysis. In inverse direction the results are used to define minimal requirements for the model accuracy and the numerical effort in order to provide a sufficient quality of the prognosis to derive decisions regarding repair, maintenance etc. of the complex systems.

在这个项目中，理论和算法的基础上开发，以分析工业规模的复杂系统的时间依赖可靠性与高的数值效率和接近现实。这些基本发展对于科普我们工业系统迅速增长的复杂性并确保其可靠性尤为重要。这些系统，如供水和污水系统、工业和生产设施、建筑物或建筑工地或车辆中的控制系统、发电厂或结构系统，对我们社会和日常生活的功能越来越重要。计划中的项目致力于确保这些系统的可靠性。设想的发展解决了目前的方法和技术的关键差异;对于工业规模的系统，它们需要在模型的非常强大的简化和与详细建模相关的不可行的数值工作之间达成关键的妥协。这种差异随着系统的复杂性而迅速增长，因此，造成建模中越来越多的不确定性，此外，生存特征的概念形成了发展的基础。这一概念有助于对系统进行普遍适用、强大、灵活和透明的建模。从这个概念开始，系统状态的系统评估被通过高效随机模拟实现的统计估计所取代。因此，结果的质量变得与问题的维度无关，即与系统的复杂性无关。复杂系统分析的数值效率提高了几个数量级。在此基础上，可以对复杂系统的时变可靠性进行详细而真实的分析，同时具有较高的数值效率。此外，复杂的模拟方法用于有针对性地识别关键故障场景，系统组件和系统域。以下假设是从统计方法中得出的基本发现。在分析复杂系统时，复杂性的增加并不一定需要分析更多的系统状态，以确保有关系统行为的分析质量一致。这一假设的有效性在项目中得到了验证和证明。建模中的不确定性和缺乏信息，这是由于日益复杂，在不精确概率的概念的帮助下定量捕获，并在可靠性分析中考虑到。在反方向上，结果用于定义模型精度和数值工作的最低要求，以便提供足够质量的预测，以得出关于复杂系统的维修、维护等的决策。