复杂系统的图模型多智能体协同故障诊断

为解决复杂、不确定系统的故障诊断实时推理问题,提出了基于图模型-多连片贝叶斯网络架构下多智能体协同推理的故障诊断方法。该方法将一个复杂贝叶斯网分割成若干有重叠的贝叶斯子网,使监控网络的单个智能体被抽象为一个拥有局部知识的贝叶斯网,利用成熟的贝叶斯网推理算法可完成智能体的自主推理。随后,通过重叠的子网接口进行多智能体间消息的传播,实现了多智能体协同故障诊断推理。实验结果表明了基于图模型多智能体的协同故障诊断方法的正确性和有效性。

Graphical model-based multi-Agent coordination fault diagnosis for complex system

To solve the real-time inference problem in complex, uncertain system fault diagnosis, a multi-Agent cooperative inference fault diagnosis approach based on Multiple Sectioned Bayesian Network (MSBN), which is a kind of graphical models, was proposed. This method partitioned a complex Bayesian Network (BN) into some overlapped small BNs. Each Agent, which monitored the sub-system, was abstracted as a moderate size BN which owned the local knowledge about the sub-system. Autonomous inferences can be conducted by the Agents through existing BN inference algorithms. Then the multi-Agent cooperative inference for fault diagnosis can be taken through the message propagation along the overlapped interfaces among the sub nets. The experimental results demonstrate that the proposed graphical model-based multi-Agent coordination fault diagnosis approach is correct and effective.