摘要
针对往复压缩机气阀早期故障的检测数据分布复杂,常规方法难以有效检测的问题,提出一种双演化遗传聚类检测算法。该算法引入测地线距离作为数据间关系测度,并将个体编码为代表各类别的典型样本序号的排列。基于生物进化系统的中自组织、自学习及自适应等复杂性,设计了相应的幂律选择算子、双演化交叉算子和种群的自适应更新策略来完成故障数据的聚类检测。将该算法用于两级往复压缩机气阀早期故障检测,试验结果表明,双演化遗传聚类算法。在对气阀早期故障的识别率上明显优于常用的K均值算法和遗传聚类算法,可应用到具有复杂数据分布的机电系统故障检测。
In consideration of the problem that the general clustering algorithm is ineffective in detection of reciprocating compressor early fault data with complex shape clusters,a novel double evolution genetic clustering algorithm is put forward in this work.The new approach employs geodesic distance to measure the similarity of data samples,and encodes each chromosome as a sequence of real integer numbers representing the cluster representatives.Based on the self-organizing,self-learning and self-adapting of evolution,a power law selecting operator,double evolution crossover operator and self-adapting generation strategy are designed to execute the clustering detection of fault data.The results of the experiments on early fault detection of reciprocating compressor's valve leakage show that the new algorithm is efficient and effective.Its performance of recognition is better than that of the K-means algorithm and generic genetic clustering algorithm,and can be adopted for detecting machine fault with complex data distribution.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2010年第4期384-388,共5页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(编号:50705073)
陕西省自然科学基础研究计划资助项目(编号:2007E224)
关键词
往复压缩机
故障检测
遗传算法
测地线距离
reciprocating compressor fault detection genetic algorithm geodesic distance
