核电系统故障诊断专家系统研究

Study of Expert System of Fault Diagnosis for Nuclear Power Plant

针对核电系统的故障特征建立了专家系统。专家系统中的知识采用启发式表示,知识可以表示成确定性和不确定性知识。对于确定性知识采用简单的命题逻辑的推理机制,对于不确定性知识引入了确定性因子kCF来表示知识的不确定性,并据此建立了推理机制。为了解决专家系统中知识获取的“瓶颈”问题,将Rough集理论引入到了核电系统故障诊断系统,并将基于可辨识矩阵的约简算法做了改进。在对属性重要性进行测度时,考虑了同决策类内具有同属性值属性及属性在可辨识矩阵中出现的次数等因素。用该诊断系统在仿真机上对不同的故障进行了诊断,并得到了正确的结果。

Based on the fault features of Nuclear Power Plant, the ES （expert system） of fault diagnosis has been programmed. The knowledge in the ES adopts the production systems, which can express the certain and uncertain knowledge. For certain knowledge, the simple reasoning mechanism of prepositional logic is adopted. For the uncertain knowledge, CF （certain factor） is used to express the uncertain, thus to set up the reasoning mechanism. In order to solve the “bottleneck” problem for knowledge acquisition, rough set theory is incorporated into the fault diagnose system and the reduction algorithm based on the discernibility matrix is improved. In the improved algorithm, the measure of attribute importance first calculate the attribute which have the same value in the same decision-sort, then calculate the degrees of attribute in the discernibility matrix. Several different faults have been diagnosed on some emulator with this expert system.