Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting

Abstract

Periodic detection of wheel tread is necessary for train operation safety. Reference positioning of inner side benchmark is a traditional method for tread detection but there are problems of positioning error caused by field factors in wheel tread dynamic detection such as reference tilt by hunting, foreign matter and light interference. A reference positioning method for wheel tread dynamic detection is proposed in this paper. After extracting the point cloud data through structured light calibration, center-line extraction and other algorithms, reference feature points of the inner side are segmented combined with tread features, and an iterative re-weighted least squares line fitting (IRLS-LF) method is used to realize robust positioning of the inner side benchmark. Under the dynamic tilt condition, the experimental man-machine comparison deviations of flange height and thickness based on IRLS-LF positioning results are ±0.1mm and ±0.2mm respectively, and the both deviation range widths based on LSLF positioning results and fixed-parameter algorithm positioning results are about 0.8 mm. Experimental results show that this method can effectively solve the datum positioning deviation caused by field factors, and effectively ensure the measurement accuracy and robustness of wheel tread dynamic detection.

Keywords

• wheel tread
• hunting
• iterative reweighting
• least squares
• benchmark positioning
• robustness