独立电源多智能体信息融合故障诊断方法

对独立电源系统进行故障诊断与预测研究是保证整个复杂运动装置系统安全性工程的重要环节.分析了目前独立电源故障诊断系统中存在的问题,提出采用多传感器信息融合和多智能体技术相结合的方法来提高故障诊断的可靠性和系统的扩展性.利用智能体的自主性、分布性和协作性,构建了独立电源多智能体信息融合故障诊断系统.根据独立电源故障征兆的特点,将D-S(Dempster-Shafer)证据理论引入到多神经网络的诊断结果融合技术中,阐述了多神经网络局部诊断智能体和D-S证据理论融合诊断智能体的具体实现方法.最后,以某型航空电源故障诊断为例,给出了故障实例的诊断仿真,结果表明该方法可有效提高诊断可信度.

Fault diagnosis for independent power-supply system using multi-agent information fusion

The fault diagnosis and prognosis research for Independent power-supply system(IPSS) was an important step to ensure the safety for the complex moving devices system. Current fault diagnosis system problems for IPSS were analyzed. The method of combining multi-sensor information fusion with multi-Agent was put forward to improve the diagnosis reliability and the system expansibility. A fault diagnosis system for IPSS using multi-Agent and information fusion was established based on Agent automation, distribution and collaboration. According to the fault symptom feature, Dempster-Shafer evidential theory was introduced into diagnosis result fusion. The multi-neural network local diagnosis Agent and the Dempster-Shafer evidential reasoning Agent implementation were disserted in detail. Finally, taking an aircraft electric power system as an example, the diagnostic process was simulated. The results indicate that the method can improve the diagnosis reliability.