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A decentralized model-based diagnostic tool for complex systems

机译:基于分散模型的复杂系统诊断工具

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摘要

We address the problem of diagnosing complex discrete-event systems such as telecommunication networks. Given a flow of observations from the system, the goal is to explain those observations by identifying and localizing possible faults. Several model-based diagnosis approaches deal with this problem but they need the computation of a global model which is not feasible for complex systems like telecommunication networks. Our contribution is the proposal of a decentralized approach which permits to carry out an on-line diagnosis without computing the global model. This paper describes the implementation of a tool based on this approach. Given a decentralized model of the system and a flow of observations, the program analyzes the flow and computes the diagnosis in a decentralized way. The impact of the merging strategy on the global efficiency is demonstrated and illustrated by experimental results on a real application.
机译:我们解决了诊断复杂的离散事件系统(例如电信网络)的问题。给定来自系统的观察流,目标是通过识别和定位可能的故障来解释这些观察。几种基于模型的诊断方法可解决此问题,但它们需要计算全局模型,这对于像电信网络这样的复杂系统是不可行的。我们的贡献是提出了一种分散方法的建议,该方法允许在不计算全局模型的情况下进行在线诊断。本文介绍了基于此方法的工具的实现。给定系统的分散模型和观察流程,该程序将分析流程并以分散方式计算诊断。实际应用中的实验结果证明并说明了合并策略对整体效率的影响。

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