摘要
针对现有动车组故障识别算法存在较高故障识别误报率与漏报率的问题,提出了一种无需预设模板的动车组故障识别算法.将动车组图像以车厢为单位进行划分,以当前列车的其他车厢作为历史图像进行图片比对,确定出故障区域所在的位置.对多种车型的模拟分析结果表明,所提算法在保障检测漏报率的基础上,误报率可降低至少20%.此外,针对复兴号列车,该算法在保持漏报率为12%的条件下,几乎可达到零误报.
Aiming at the problem of existing electric multiple units(EMU)fault recognition algorithms with relatively higher false alarm rate and missing alarm rate,an EMU fault recognition algorithm without preset template was proposed.The EMU images were divided into compartments,and the other compartments of current train were used as historical images for comparison,so as to determine the location of fault area.The simulation and analysis results for several vehicle models show that the as-proposed algorithm can reduce the false alarm rate by at least 20%on the basis of ensuring the missing alarm rate.In addition,for China rejuvenation train,the algorithm can achieve almost zero false alarm rate,when keeping the missing alarm rate at 12%.
作者
李超
刘子宽
LI Chao;LIU Zi-kuan(Institute of Electronic Computing Technology,China Academy of Railway Sciences Group Co.Ltd.,Beijing 100081,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2021年第5期563-568,共6页
Journal of Shenyang University of Technology
基金
国家自然科学基金项目(62001519)
中国铁路总公司科技研究开发计划项目(P2018J016)
中国铁路广州局集团有限公司信息化专项科研项目(2018X12-Y).
关键词
智能交通
动车组故障识别算法
预设模板
故障自动检测
高斯混合模型
SIFT特征匹配
图像匹配
非极大值抑制
intelligent transportation
EMU fault recognition algorithm
preset template
automatic fault detection
Gaussian mixture model
SIFT feature matching
image matching
non-maximum suppression
