基于分布式贝叶斯网络的多故障诊断方法研究

摘要

针对复杂系统存在的不确定性、多故障以及传统贝叶斯网络诊断实时性差等问题，提出一种基于分布式贝叶斯网络的故障诊断方法。该故障诊断方法将大型、复杂系统故障诊断模型抽象为贝叶斯网络模型，并将其分解为若干贝叶斯网络子系统，基于消息传播机制完成多个子系统局部推理以及子系统间重叠子域紧凑的消息传播，实现分布式贝叶斯网络的故障推理与诊断。实验结果表明，该故障诊断方法可在复杂、不确定性系统中完成单故障和多故障推理、诊断任务，与传统贝叶斯网络故障诊断方法相比，该方法在推理速度上的优势尤为突出，具有广泛的应用前景。%Since the complex system has the uncertain and multi?fault problems,and the traditional Bayesian network diag?nosis has poor real?time performance,a fault diagnosis approach based on distributed Bayesian network is proposed. The large?scale and complex system fault diagnosis model is abstracted as the Bayesian network model with the fault diagnosis method. The Bayesian network model is decomposed into several Bayesian network subsystems. The information propagation mechanism is used to accomplish the partial inference of the multiply subsystems and compact information propagation of the overlap subdo?main among subsystems,and realize the inference and diagnosis of the distributed Bayesian network fault. The experimental re?sults demonstrate that the fault diagnosis method can accomplish the single fault and multi?fault diagnosis inference and diagno?sis task in the complex and uncertain system. In comparison with the traditional Bayesian network fault diagnosis method,the method has the prominent advantage of fast inference speed and extensive application prospects.