Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting
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,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2022-02-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.01.014 |
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| _version_ | 1849224946087100416 |
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| author | LI Miaocheng WANG Junping SHEN Yunbo YOU Yong DAI Bowang LAN Qiangqiang |
| author_facet | LI Miaocheng WANG Junping SHEN Yunbo YOU Yong DAI Bowang LAN Qiangqiang |
| author_sort | LI Miaocheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4dba55786bdc449ca4d82307d429c8c4 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-4dba55786bdc449ca4d82307d429c8c42025-08-25T06:48:51ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272022-02-01899623763454Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line FittingLI MiaochengWANG JunpingSHEN YunboYOU YongDAI BowangLAN QiangqiangPeriodic 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.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.01.014wheel treadhuntingiterative reweightingleast squaresbenchmark positioningrobustness |
| spellingShingle | LI Miaocheng WANG Junping SHEN Yunbo YOU Yong DAI Bowang LAN Qiangqiang Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting Kongzhi Yu Xinxi Jishu wheel tread hunting iterative reweighting least squares benchmark positioning robustness |
| title | Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting |
| title_full | Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting |
| title_fullStr | Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting |
| title_full_unstemmed | Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting |
| title_short | Wheel Tread Dynamic Detection Benchmark Positioning Method Based on Iterative Reweighted Least-squares Line Fitting |
| title_sort | wheel tread dynamic detection benchmark positioning method based on iterative reweighted least squares line fitting |
| topic | wheel tread hunting iterative reweighting least squares benchmark positioning robustness |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2022.01.014 |
| work_keys_str_mv | AT limiaocheng wheeltreaddynamicdetectionbenchmarkpositioningmethodbasedoniterativereweightedleastsquareslinefitting AT wangjunping wheeltreaddynamicdetectionbenchmarkpositioningmethodbasedoniterativereweightedleastsquareslinefitting AT shenyunbo wheeltreaddynamicdetectionbenchmarkpositioningmethodbasedoniterativereweightedleastsquareslinefitting AT youyong wheeltreaddynamicdetectionbenchmarkpositioningmethodbasedoniterativereweightedleastsquareslinefitting AT daibowang wheeltreaddynamicdetectionbenchmarkpositioningmethodbasedoniterativereweightedleastsquareslinefitting AT lanqiangqiang wheeltreaddynamicdetectionbenchmarkpositioningmethodbasedoniterativereweightedleastsquareslinefitting |