机电工程技术2023，Vol.52Issue(12)：10-14,18,6.DOI:10.3969/j.issn.1009-9492.2023.00.10121
基于融合评价指标的k-means聚类算法的地铁车轮踏面磨耗分析
Metro Wheel Tread Wear Analysis Based onk-means Clustering Algorithm Based on Fusion Evaluation Index
易佳 1陆正刚1
作者信息
- 1. 同济大学铁道与城市轨道交通研究院,上海 200092
- 折叠
摘要
Abstract
The clustering idea is used to extract the features of a large number of wheel tread wear data of a subway line,the wear characteristics are further analyzed,the impact of the clustering parameter conversion method on different clustering effect evaluation indicators are studied,and a k-means mean clustering method based on fusion evaluation index is finally proposed,which solves the interference of subjective factors on the clustering effect of the clustering model when determining the number of clusters.The results show that the cluster feature transformation of the weighting method is carried out by using the variance of the corresponding clustering feature parameters;the subway wheel tread is clustered into five categories,the average method is used to divide the five typical wear profiles,and the effectiveness of the clustering method is further verified by calculation and analysis based on the wheel shape data at different time nodes.It provides reference for the economic repair strategy of metro wheel tread.关键词
融合评价指标/聚类分析/影响因素/车轮踏面镟修Key words
convergence evaluation indicators/cluster analysis/affecting factor/wheel reprofiling分类
计算机与自动化引用本文复制引用
易佳,陆正刚..基于融合评价指标的k-means聚类算法的地铁车轮踏面磨耗分析[J].机电工程技术,2023,52(12):10-14,18,6.
