摘要
为解决多级往复式压缩机故障诊断这一复杂问题 ,提出了一种基于智能互补融合的智能诊断策略 .该策略利用粗糙集理论对数据样本进行浓缩 ,形成初步的诊断规则 ,并基于该结果形成模糊神经网络 ,再利用网络的分类逼近能力 ,建立从故障状态空间至解释空间的精确映射 ,从而达到故障诊断的目的 .另外 ,还提出了一种基于误差反馈的节点函数特性变化模糊神经网络逼近器和新的数据浓缩度量指标———数据蒸发率 .对一台四级压缩机的故障诊断结果表明 ,提出的新方法具有诊断率和数据蒸发率高、结果易于被人理解、诊断计算量小等优点 .
Based on complementary strategies, a new AI method was put forward to solve the fault diagnosis of the multilevel reciprocating compressor. Using rough set theory to enrich the data, then exploiting fuzzy neural network(FNN) to perform diagnosing, the diagnosing rules could be obtained from the network. Additionally, a new FNN approach method and a criterion about the data enriching were presented. The correctness and the effectiveness were showed in the experiment.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2001年第9期940-944,共5页
Journal of Xi'an Jiaotong University
基金
教育部留学回国人员科研起动基金资助项目
