Adaptive modeling method based on the Fast-MCD to analyze railway track irregularity deterioration

YANG Yaqin; XU Peng; WU Xishui

doi:10.16511/j.cnki.qhdxxb.2021.26.044

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Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (3) : 516-522. DOI: 10.16511/j.cnki.qhdxxb.2021.26.044

SPECIAL SECTION: INTELLIGENT TRANSPORTATION

Adaptive modeling method based on the Fast-MCD to analyze railway track irregularity deterioration

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Abstract

Railway track predictive maintenance needs to identify the track irregularity deterioration mode. For this, the track irregularity deterioration process is split into maintenance periods according to the maintenance records to analyze the deterioration mode. A rail track irregularity deterioration adaptive piecewise modeling framework was developed. In order to improve the efficiency and accuracy of the framework, a two-level fast minimum covariance determinant (Fast-MCD) was developed based on the Mahalanobis distance. The critical values for each level were determined for a specific case by measurements along the Nanchang-Fuzhou railway from 2013 to 2020. Analysis of the data showed that the efficiency is improved by nearly 50% and the identification accuracy of the maintenance dates is improved by nearly 45.3% through use of the two-level Fast-MCD. The track irregularity deterioration mode was further studied by using this framework to analyze the track longitudinal deterioration of each track unit for each maintenance period with a total of nearly 6 000 maintenance periods. The goodness-of-fit and prediction capabilities of linear, exponential, and logarithmic functions were compared for each maintenance period. The results show that the linear function best describes the track longitudinal irregularity deterioration.

Key words

Mahalanobis distance / Fast-MCD / adaptive modeling / track irregularity deterioration

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YANG Yaqin, XU Peng, WU Xishui. Adaptive modeling method based on the Fast-MCD to analyze railway track irregularity deterioration[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(3): 516-522 https://doi.org/10.16511/j.cnki.qhdxxb.2021.26.044

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