ABSTRACT The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

中文翻译：

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基于整体代理的具有随机依赖性的系统级剩余使用寿命估计方法

摘要 复杂系统中新出现的行为比其组成部分的行为总和更为复杂。此行为涉及零件之间故障的传播，并需要有关零件如何相关的信息。因此，系统级的预测功能成为一项非常艰巨的任务。传统方法侧重于识别故障及其发生的概率。在复杂系统中，这会对预测功能造成统计限制，其中组件故障依赖于系统中连接的组件及其退化状态。在本文中，提出了一种新的基于 Holonic 代理的方法，用于具有不同依赖性的系统级剩余使用寿命（S-RUL）估计。由于所提出的方法可以捕获系统中发生的故障/故障模式传播和交互，一直到组件和最终系统级别，因此它可以作为可靠性任务中的自动测试工具。通过一个数值例子，在考虑和不考虑随机依赖的Java代理开发环境中实现。结果表明，影响组件的间接影响对 S-RUL 有巨大的影响，随机依赖的影响不容忽视，尤其是在系统设计的早期阶段。实现是在 Java 代理开发环境中完成的，考虑和不考虑随机依赖性。结果表明，影响组件的间接影响对 S-RUL 有巨大的影响，随机依赖的影响不容忽视，尤其是在系统设计的早期阶段。实现是在 Java 代理开发环境中完成的，考虑和不考虑随机依赖性。结果表明，影响组件的间接影响对 S-RUL 有巨大的影响，随机依赖的影响不容忽视，尤其是在系统设计的早期阶段。