复杂场景下动车底部螺栓丢失故障的自动检测  

Automatic Inspection of Bolt Missing at the Bottom of Multiple Unit Trains Under Complex Environment

作　　者：路绳方 

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100083

出　　处：《激光与光电子学进展》2017年第11期291-297,共7页Laser & Optoelectronics Progress

基　　金：国家重大科学仪器设备开发专项(2012YQ140032);科学研究与研究生培养共建项目-成果转化与产业化项目-列车弓网运行状况在线动态检测系统

摘　　要：动车底部闸瓦部位的螺栓,对列车整体制动系统起着关键作用。闸瓦部位螺栓的丢失,会给列车安全制动以及安全行驶带来严重威胁。以螺栓丢失故障检测为例,提出动车中零部件丢失故障的在线检测与识别算法,为动车重点部位的故障诊断进行针对性检测提供了一种指导方法。结合螺栓几何结构的特点,提出了一种基于图像Sobel梯度边缘的完备局部二进制模型特征提取算法,结合二值分类器的训练与学习,完成螺栓丢失故障的自动检测。结果表明,所提算法对复杂场景下螺栓丢失故障的识别有很强的稳健性,其检测效率和精度也很高,能够满足现场应用需求。The bolt at the bottom of multiple unit train plays a key role in the overall braking system of the train. The bolt missing will bring serious challenges for the train safety braking and safety running. By the example of fault inspection of bolt missing, an online inspection and recognition algorithm is proposed for the fault of components missing in a train, which provides a guidance for the targeted inspection on the key parts of the train. Due to the characteristics of the bolts geometry, a complete local binary patterns feature extraction algorithm based on the image Sobel gradient edge is proposed, and the training and learning of a binary classifier is combined to complete the automatic fault inspection of bolt missing. The results show that the proposed algorithm has strong robustness to inspect the fault in complex scenes with high inspection efficiency and precision, which can meet the demand of the site application.

关 键 词：机器视觉 故障检测 特征提取 二值分类器 螺栓 

分 类 号：U216.3[交通运输工程—道路与铁道工程]