基于MAS的混合智能故障诊断模型研究

MAS-based Hybrid Intelligent Fault Diagnostic Model

针对复杂设备故障的复杂性和不确定性等特点,以起重设备为研究对象,提出了一种基于多Agent的混合智能故障诊断模型,并构建了模型的诊断工作过程及每一功能Agent的内部结构和功能。模型根据各子系统并行感知外界环境,动态地组建Agent诊断组,共同完成诊断任务。最后,通过实例验证了模型的有效性。该模型在某企业的设备远程维护平台应用中,能快速、准确地进行故障成因分析,并给出合理的决策意见,提高了设备的安全运行效率。

An Agent-based Distributed Intelligence Fauh Diagnostic Model is proposed for solving fault diagnosis problems of complex hoisting equipments, based on the complexity and uncertainty of the faults. The working process of the model and the internal structure of each functional agent are established. Agents are assembled to accomplish diagnostic tasks according to the faults apperceived by subsystems in parallel. An example is used to illustrate and verify the effectiveness of the model. When applied on a remote equipment maintenance system of an enterprise, the model can offer us a quick and accurate analysis, giving us reasonable, constructive and decisive advice and enhancing the safe running efficiency of the equipment.