Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network  

作　　者：Yuanhang Wang Duxin Liu Sheng Guo Yifan Wu Jing Liu Wei Li Hongjie Wang 

机构地区：[1]Robotics Research Center,School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing 100044,China [2]China Construction Industrial&Energy Engineering Group Co.,Ltd.,Nanjing 210023,China

出　　处：《Chinese Journal of Mechanical Engineering》2024年第2期356-371,共16页中国机械工程学报（英文版）

基　　金：Supported by Fundamental Research Funds for the Central Universities of China(Grant No.2023JBMC014).

摘　　要：In the railway system,fasteners have the functions of damping,maintaining the track distance,and adjusting the track level.Therefore,routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines.Currently,assessment methods for fastener tightness include manual observation,acoustic wave detection,and image detection.There are limitations such as low accuracy and efficiency,easy interference and misjudgment,and a lack of accurate,stable,and fast detection methods.Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening,this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks.First,the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration.Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod.The point is projected onto the upper surface of the bolt to calculate the projection distance.Subsequently,the mapping relationship between the projection distance sequence and fastener tightness is established,and the influence of each parameter on the fastener tightness prediction is analyzed.Finally,by setting up a fastener detection scene in the track experimental base,collecting data,and completing the algorithm verification,the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm,which significantly improved the effect compared with other tightness detection methods,and realized an effective fastener tightness regression.

关 键 词：Railway system Fasteners Tightness inspection Neural network regression 3D point cloud processing 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH17[自动化与计算机技术—控制科学与工程]