基于BPSO的多故障最小候选集生成技术

多故障最小候选集生成是制定多故障诊断策略的首要步骤。利用二进制粒子群优化算法（binary particle swarm optimization, BPSO）生成多故障模糊组的最小候选集。首先，利用紧集表示法描述某或节点上的多故障模糊组，其最小候选集即多故障模糊组的最小碰集|然后，利用BPSO算法求解多故障模糊组的最小碰集，通过构造个体适应度和群体适应度双函数，解决BPSO算法求解最碰集的适应性问题，并保证了算法尽可能搜索冲突集的全部碰集|最后，通过某系统实例对算法的有效性进行了验证。事实表明，该方法能有效应用于多故障最小候选集问题的求解。

Generation of minimal candidate set for multiple fault diagnosis based on binary particle swarm optimization

The generation of a multiple fault minimal candidates set is the first step in making the multiple fault diagnostic strategy.The binary particle swarm optimization(BPSO) algorithm is applied to generate the minimal candidates set.Firstly,the multiple fault ambiguity group in a certain OR node is described by using compact set notation,the minimal candidate set is a minimal hitting set.Then,by constructing both individual fitness function and swarm fitness function,the BPSO is applied in finding minimal hitting set successfully,and guarantees to find the hitting sets as many as possible.Finally,the experiment results of a certain real system verify the effectiveness of the algorithm,which proves that this method can be applied in solving the minimal hitting set of multiple fault diagnosis effectively.