基于模型诊断的改进贝叶斯方法

基于模型诊断是针对系统或设备的行为和结构建立模型,从而进行诊断的.但是基于模型诊断的方法存在不确定性问题,诊断的结果可能为一组故障部件.为解决不确定性问题,很多学者在基于模型诊断中使用了概率的方法,利用待诊断设备组成部件的故障概率信息来寻找最可能的诊断.通过对模型诊断中存在的不确定性问题的深入研究,在基于模型诊断中提出了概率的贝叶斯解释,从而利用后验概率形式量化了元件故障的可能性的衡量标准,并且改进了计算元件后验概率的方法,分析了改进后算法的复杂性和完备性,证明了改进后的方法降低了时间和空间的复杂性.实验结果表明,改进后算法的执行效率较原有的算法有明显的提高,且有些问题可以提高两个数量级.

Improved Bayesian Method for Model-based Diagnosis

Model-based diagnosis concerns using a model of the structure and behavior of a system or device in order to establish why the system or device is faulty. But the fact is that determining a diagnosis for a problem always involves uncertainty. This situation is not entirely satisfactory. This paper built upon and extended previous work in model-based diagnosis by supplementing the model-based framework with probabilistic sound ways for dealing with uncertainty. This was done in a mathematically way in I3ayesian theory to compute the posterior probability that a certain component is not working correctly given some diagnosis. And in this paper we proposed a general method to increase efficiency. The complexity and the completeness of the method were analyzed. I}he time complexity and the space complexity were reduced in the improved method. The experimental results illustrate that the improved method has a better executive efficicncy than the traditional method in general. In fact, the executive efficiency may be improved up to two orders of magnitude in some cases.