人工鱼群算法在矿井提升机故障诊断中的应用[EB/OL]
北京：中国科技论文在线
） 摘要： 基于因果模型的诊断方法是人工智能领域发展起来的一个十分活跃的分支
在该方法中,由故障症状集求解极小故障集的过程是一个NP-Hard问题
通过对诊断问题进行更精确的数学建模和分析, 提出了将概率因果模型和人工鱼群算法相结合的矿井提升机的故障诊断方法,该方法将因果模型的似然函数作为人工鱼群算法的目标函数,从而将复杂系统的故障诊断转化为最优问题
通过将问题映射到0/1整数规划问题, 对多绳摩擦提升机运行中典型的过卷故障进行分析
仿真结果表明,该方法能够适应诊断过程中出现的不确定性,并实现多故障诊断,在故障症状集基数大于20的情况下能够求出95%-100%的极小诊断
关键词： 矿井提升机
人工鱼群算法
夏士雄
夏士雄
故障诊断
） Abstract： Probabilistic Causal-effect Model-Based diagnosis is an active branch of Artificial Intelligent
The method is a NP-Hard problem resolving minimal diagnosis sets from fault symptom sets
The problem is precisely defined in this paper and a novel diagnosis method is proposed based on probabilistic causal—effect model and Artificial fish-swarm algorithm (AFSA),which takes advantage of calculating probability function instead of the original complex system．According to the typical over-convoluted fault in the operation of friction hoist with many steel ropes, the method is tested by mapping hitting sets problem to 0/1 integer programming problem
simulation results show that it can deal with the uncertainty situation and be suitable for multi—faults diagnosis, This method has higher reliability and practicability by analyzing the actual operational condition in the mine’s exaltation．It can get 95% to 100% minimal diagnosis in conditions that number of fault symptom sets larger than 20
Keywords： mine hoist; artificial fish-swarm algorithm; fault Diagnosis; probabilistic causal-effect model 下载PDF阅读器 PDF全文下载： 初稿 （ 173 ） 作者简介： 通信联系人： 【收录情况】 中国科技论文在线： 汪楚娇
概率因果模型 WANGChu-Jiao *
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