基于免疫遗传算法的核动力船舶设备故障诊断

Fault diagnosis of nuclear power ship components based on immune genetic algorithm

针对传统遗传算法收敛速度慢且容易未成熟收敛引起的核动力船舶设备故障诊断响应延迟、误诊、漏诊问题,提出一种基于信息熵的免疫遗传算法用于核动力船舶设备的故障诊断:利用已知船舶设备故障征兆集合,选用概率因果模型,引入信息熵免疫遗传算法,求解具有最大后验概率的故障集合。某船用核动力蒸汽发生器与液压泵故障仿真结果表明,基于信息熵免疫遗传算法优化的概率因果模型不受故障样本的限制,具有较好的通用性,且模型故障诊断精度较高、寻优速度快。本方法同样适用于其他领域的故障诊断问题。

Because the slow convergence rate and premature convergence of the traditional genetic algorithm, fault dia- gnosis of nuclear power equipment has been delayed response, misdiagnosed, missed diagnosed and so on. An immune ge- netic algorithm based on information entropy for fault diagnosis of nuclear power systems was proposed： By using the known nuclear power fault symptom sets, the probability causal model was selected, and the information entropy immune genetic algorithm was introduced to solve the fault sets which had the maximum a posteriori probability. The simulation res- ults of the nuclear steam generator and hydraulic pump faults showed that the probabilistic causal model based on the inform- ation entropy immune genetic algorithm was not restricted by the fault samples. This method had good generality, high ac- curacy, and fast searching speed, the process can also be applied to fault diagnosis in other fields.