基于因果规则的电力营销系统故障定位算法

现有的故障定位算法无法有效地应用于带有负载均衡机制的因果关系频繁变动的复杂系统。为此，本文提出一种基于因果规则的故障定位算法（CRFLA）。首先利用改进的因果关联兴趣度度量方法自适应地学习出故障和事件之间因果规则，然后根据得到的因果规则中故障原因集对已发生事件集的影响程度进行根因推断。该方法考虑了因果关系的同时无需明确具体的因果网络结构，并且能够灵活地结合先验知识。利用电力营销系统中真实生产环境产生的数据进行故障定位，实验结果表明，CRFLA优于传统的方法，能够迅速、有效地定位故障根因。

Fault Location Algorithm for Electric Power Marketing System Based on Causal Rules

Existing fault location algorithms cannot be effectively applied in complex systems with load balancing mechanisms in which causality changes frequently. To this end, a fault location algorithm based on causal rules (CRFLA) is proposed. Firstly, the causal rules between faults and events are adaptively learned by the improved causal association interesting measure method, then the root cause is inferred according to the influence degree of the fault cause set on the occurred event set. The method considers causality without the need to specify a specific causal network structure, and can flexibly combine prior knowledge. Using the data generated by the real production environment in the power marketing system to locate the fault, the experiment results show that CRFLA is superior to the traditional method, and can quickly and effectively locate the root cause.