A study of error correction method based on selective focus for curved rail profile measurement

Wang, Le; Wang, Fan; Liu, Zhengyi; Fang, Yue; Han, Qiang; Wang, Shengchun; Wang, Hao; Zhou, Qian; Li, Guoqing; Ren, Shengwei; Wang, Ning

doi:10.1016/j.optlastec.2023.110124

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Nonetheless, incorporating additional lasers can result in sophisticated machinery configurations and potential maintenance obstacles, thereby restricting its usability in real-world scenarios. To avoid problems caused by multi line lasers, Wang et al [26] introduced a novel error correction approach for measuring curved rail profiles. They utilized selective focus to obtain a plane perpendicular to the LDR, offering a fresh perspective for future research on correcting measurement errors in rail profile analysis.…”

Section: Introductionmentioning

confidence: 99%

Error correction method based on dual-beam laser for curved rail profile measurement

Liu,

Wang,

Cheng

et al. 2024

Meas. Sci. Technol.

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The current method of dynamic rail profile measurement involves the installation of a line-structured light sensor at the base of the train. The accuracy of this measurement is influenced by the vertical relationship between the laser plane of the light sensor and the longitudinal direction of the rail (LDR). When a train travels in a straight line, the normal of the laser plane aligns with the LDR. However, when the train curves, the angle at which its wheels connect with the rails causes the laser plane's normal direction to deviate from the LDR, leading to measurement errors. To address this issue, we propose a method for curved rail profile measurement using a dual-beam laser to correct these errors. This method involves generating an auxiliary 3D rail reflecting the LDR and a virtual 3D rail reflecting the normal direction of the laser plane from the cross-section image of the dual-beam laser. An optimization function is then formulated to determine the optimal auxiliary plane (optimal-AP) by analyzing the alignment or intersection between the auxiliary and virtual 3D rails. Distorted contour points are projected onto the optimal-AP to rectify errors. Experiments validate the accuracy and effectiveness of this proposed method. The results show that, regardless of pitch or yaw movement between the laser plane and the LDR, the error in measuring corrected profile wear remains consistently below 0.10 millimeters, thereby meeting the accuracy standard for rail wear measurement. This approach rectifies measurement errors in curved rail profiles from a 3D perspective, ensuring accurate measurements even under complex working conditions. It also provides a valuable reference for error analysis and improving dynamic rail profile measurement accuracy.

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Section: Introductionmentioning

confidence: 99%

Error correction method based on dual-beam laser for curved rail profile measurement

Liu,

Wang,

Cheng

et al. 2024

Meas. Sci. Technol.

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“…In recent years, there has been a lot of new research on error methods for profile error evaluation, with rapid updates and iterations in terms of measurement accuracy, evaluation speed, and other aspects. Wang [8] proposed a curve track profile measurement error correction method based on selective focusing for on-board track profiles, achieving correction of curve profile measurement errors. Yu [9] proposed a new compensation method based on real-time wheel profile and residual error in normal direction of grinding wheel to improve the shape accuracy of grinding wheel profile.…”

Section: Introductionmentioning

confidence: 99%

Small-sample linear profile error uncertainty assessment based on grey system

Zhang,

Chen,

Zhang

2024

Meas. Sci. Technol.

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The uncertainty assessment of the profile error of the cam profile, as defined in the national standard method, is difficult to carry out under conditions of small sample size and absence of probability distribution assumptions. This paper proposes a small-sample assessment model for the uncertainty of the profile error based on grey system. Firstly, the coordinate transformation is conducted using Vector Alignment Method（VAM）to reduce systematic errors, and the Non-Uniform Rational B-splines(NURBS)curve interpolation is utilized to fit the cam profile curve and perform error assessment. Subsequently, based on the error assessment results, Grey Information Measurement Model (GIMM) for the uncertainty of the profile error in small samples is established. This model employs Grey Relational Analysis (GRA) to eliminate outliers and evaluates the uncertainty of the profile error by solving grey correlation coefficients. Maximum-Minimum Information Measure Method (MIMM) is used to assess the optimal sample size. Finally, numerical experiments and experimental tests were conducted on the uncertainty of camshaft profile error in automobiles. A total of 15 sets of profile data were compared with Guide to the Representation of Uncertainty in Measurement(GUM) and Monte Carlo Method (MCM) under different sample sizes. The results showed that GIMM achieved evaluation with only 8 sets of data samples under small sample and poor information conditions, with an uncertainty of 0.6338, compared to 0.6346 for GUM and 0.6391 for MCM. The acceptance rate of GIMM reached 95.2%. This model outperforms other methods, providing a simplified and reliable assessment of cam profile error uncertainty.

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A high-precision visual detection method for rail profile based on Scheimpflug condition

Zhan

2025

Measurement

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A study of error correction method based on selective focus for curved rail profile measurement

Cited by 3 publications

References 14 publications

Error correction method based on dual-beam laser for curved rail profile measurement

Error correction method based on dual-beam laser for curved rail profile measurement

Small-sample linear profile error uncertainty assessment based on grey system

A high-precision visual detection method for rail profile based on Scheimpflug condition

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