汽轮机故障诊断的因果网络群智能算法模型

Swarm Intelligence Algorithm Model of Causal Network in Fault Diagnosis of Turbine Machinery

在利用概率因果网络模型进行汽轮机故障诊断过程中,传统的基于简约覆盖理论的求算方法不能直接得到诊断问题的解,而且当故障节点较多或网络层次较多时,存在着"组合爆炸"、计算量呈指数速度增加等问题;对此,基于群智能理论,建立汽轮发电机组故障诊断系统概率因果网络群智能算法模型,给出基本的群运算法则及改进方法,将焦点放在有限空间的诊断推理上,并实现并行处理,很好地解决了传统算法中的问题;最后一个应用实例验证了此方法的优越性及工程实用性。

In the procedure of turbine machinery fault diagnosis by probabilistie causal network model, the solution of the diagnostic problem can not be directly gotten by the traditional solving method based on parsimony covering theory and, furthermore, multi-level, multi-node complicated causal network may lead to the problem of ＂combinatorial explosion＂ and the exponential increase in computational cost etc. To solve these problems, based on the theory of swarm intelligence, the swarm intelligence algorithm model of probabilistic causal network in turbine machinery fault diagnosis system is established, and the basic swarm calculation method and the improved method is given. The problems in the traditional solving method are well settled by focusing on finite reasoning space and parallel processing. Finally, the advantages and the engineering practicability are demonstrated by a practical example.