面向系统级故障诊断的高效遗传算法

Elhadef和Ayeb首次提出采用遗传算法来进行系统级故障诊断,其适应度函数通过比较实际症候与当前猜测故障集产生的症候得到.上述算法的一个缺点是其适应度函数只考虑了故障集随机生成的一个症候,因而会漏掉绝大多数有效的故障集.对此首先针对PMC模型提出结点状态与诊断图中一定症候相容时结点状态应满足的方程,然后通过设计基于该方程的适应度函数,提出针对t-可诊断系统的遗传算法.理论分析和模拟实验均表明文中算法在迭代步数上大大地优于原算法.此外,还确认了Elhadef提出的产生初始种群的方法的高效性.

An Efficient Genetic Algorithm for System-Level Diagnosis

Elhadef and Ayeb devised a genetic algorithm for the system-level diagnosis of multi- computers,where the fitness function is calculated by comparing the given syndrome with the syndrome randomly produced by the current guess fault set.One demerit of this algorithm is that this fitness function takes only one syndrome from many possible candidates,leading to a high probability of incorrect diagnosis.In the present paper,the authors describe a set of equations that govern the statuses of the units in a system.Based on this,the authors present a new genetic algorithm for the fault diagnosis of diagnosable systems by designing a novel fitness function. Theoretical analysis and simulation result both show that the algorithm is remarkably superior to the Elhadef-Ayeb's diagnosis algorithm in terms of the number of iterations.The initial popula- tion production process proposed by Elhadef and Ayeb is also justified.