适用于核动力设备故障诊断的改进粒子群优化算法

An Improved Particle Swarm Optimization Algorithm for Fault Diagnosis of Nuclear Power Equipment

提出一种基于改进粒子群优化(IPSO)算法的核动力设备故障诊断方法.利用已知核动力设备故障征兆集合,选用概率因果模型求解具有最大后验概率的故障集合;在传统粒子群优化(PSO)算法的基础上,利用佳点集原理对PSO算法进行初始化,优化了粒子群的初始化范围;借助自适应调整的惯性权重法,避免PSO算法未成熟收敛,加快了收敛速度.最后通过算例证明该方法的有效性.结果表明:基于改进粒子群优化算法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.

An improved particle swarm optimization （IPSO） algorithm was proposed for fault diagnosis of nuclear power systems. By using the known symptom sets of nuclear power faults, and with the probabilistic causal model, the IPSO algorithm was introduced to solve the fault sets with maximum a posteriori probability; based on traditional PSO algorithm, the principle of good point set was used to initialize the range of PSO; by adaptive adjustment of inertia weight, the premature convergence of PSO was avoided, and the convergence speed was accelerated. Finally, the validity of the method was demonstrated by examples. Results show that the probabilistic causal model based on IPSO algorithm is not limited by fault samples, which therefore has good versatility, with high precision in fault diagnosis and high speed in optimization.