摘要
针对滚动轴承早期故障识别率不高、识别速率慢等问题,结合人工蜂群全局搜索能力强和相似性匹配精确的特点,设计了一种改进人工蜂群算法。该算法通过人工蜂群的组合权重相似性匹配对故障聚类中心进行全局搜索,采用均值迭代更新确定最优解;以共享信息的方式快速定位故障位置。试验表明,相对于其他相似性测度方法,改进人工蜂群算法的识别率及计算效率均有所提高,对轴承早期故障的诊断效果较佳。
In view of the problems about low identification accuracy and slow recognition rate for early faults in rolling bearings, an improved similarity matching algorithm of artificial bee colony （IABC） is design combined with strong global search ability and precise similarity matching characteristics of artificial bee colony. In this algorithm, the global search of the fault clustering center is carried out by the similarity matching of the combination weights of the artificial bee colony. The optimal solution is determined by means of mean iterative update, and the fault location is quickly located in the way of sharing information. Experimental results show that compared with other similarity measures, the recognition rate and the computational efficiency are improved for the improved artificial bee colony algorithm.
作者
薛勇
万振刚
Xue Yoag Wan Zhengang(School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China)
出处
《轴承》
北大核心
2017年第5期45-48,共4页
Bearing
关键词
滚动轴承
故障诊断
人工蜂群
相似性匹配
权重
特征提取
rolling bearing
fault diagnosis
artificial bee colony
similarity matching
weight
feature extraction
