基于蝙蝠算法的系统级故障诊断研究

首次将蝙蝠算法用于解决系统级故障诊断问题,从而提出了一种高效的诊断算法——蝙蝠故障诊断算法。在初始化阶段,种群被分成大、小两类,并采用不同的处理方式;根据系统级故障模型的特点,设计出了具有方程约束的适应度函数;为了平衡全局搜索与局部搜索,在速度更新公式中增加一个变系数;为实现寻址的离散化,对蝙蝠速度进行了二进制映射。仿真实验结果表明,蝙蝠故障诊断算法在迭代次数、诊断正确率和最优解的适应度等方面明显优于现有的具有代表性群智能诊断算法——FAFD算法。

System level fault diagnosis based on bat algorithm

In this paper we apply the bat algorithm to solving the system level fault diagnosis problem for the first time as an effective diagnosis algorithm. During the initialization phase, the population is divided into two categories: large and small, and they are handled in different ways. An equation constrained fitness function is designed according to the characteristics of the system level fault model. To balance global search and local search, a variable coefficient is added to the velocity updating formula. We also perform binary mapping for bat speed to achieve the discretization of the addressing mode. Simulation results show that using the bat algorithm for fault diagnosis has significant advantages over FAFD, a typical representative of swarm intelligence diagnosis algorithms in aspects of the number of iterations, diagnostic accuracy and fitness of optimal solution.