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首页> 外文会议>IAF Space Systems Symposium;International Astronautical Congress >Evidence-Based Resilience Engineering of Dynamic Space Systems
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Evidence-Based Resilience Engineering of Dynamic Space Systems

机译：基于证据的动态空间系统恢复力工程

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This paper will present a method for the design for resilience of complex systems under epistemic uncertainty when the characteristics of the subsystems are time-varying. In this approach, the complex system is modelled as a network of interconnected nodes, each of which is characterised by one or more quantities of interest. The quantities of interest of each subsystem are dependent on a number of decision and uncertain variables that are strictly related only to that subsystem. A set of scalar quantities, called coupling functions, exchange information between pairs of subsystems. Each pairing function is dependent on a set of coupling uncertain parameters. The uncertainty associated to all uncertain variables is modelled using Dempster-Shafer theory of evidence. Thus the network is called Evidence Network Model (ENM). This work in particular will consider the case in which the quantity of interest of each subsystem has a state that depends on the uncertainty and can change with time. In this way we can simulate continuous transitions between fully functioning and degraded states and the effect of disruptions and shocks that can perturbed the system. One of the quantities of interest is the mass of the subsystem that we will use as generic performance indicator of the overall system. Hence, the value of the ENM is the sum of the individual masses of each subsystem. The problem is, therefore, to minimise the system mass under uncertainty while all the other quantities of interest are concurrently optimised.
机译：本文将提出一种当子系统的特性是时变的，在认知不确定性下复杂系统弹性设计的方法。在这种方法中，复杂系统被建模为一个互联节点网络，每个节点都有一个或多个感兴趣的数量。每个子系统的感兴趣量取决于许多决策和不确定变量，这些变量仅与该子系统严格相关。一组称为耦合函数的标量在成对的子系统之间交换信息。每个配对函数依赖于一组耦合不确定参数。与所有不确定变量相关的不确定性采用邓普斯特-沙弗证据理论建模。因此，该网络被称为证据网络模型（ENM）。这项工作将特别考虑的情况下，每个子系统的兴趣量有一个状态，取决于不确定性，并且可以随时间变化。通过这种方式，我们可以模拟完全功能状态和降级状态之间的连续转换，以及可能干扰系统的中断和冲击的影响。感兴趣的数量之一是子系统的质量，我们将其用作整个系统的通用性能指标。因此，ENM的值是每个子系统的单个质量之和。因此，问题是在不确定的情况下最小化系统质量，同时同时优化所有其他感兴趣的量。

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