Abstract
The records of maintenance activities are required for modeling the track irregularity deterioration process. However, it is hard to guarantee the completeness and accuracy of the maintenance records. To tackle this problem, an adaptive piecewise modeling framework for the rail track deterioration process driven by historical measurement data from the comprehensive inspection train (referred to as CIT) is proposed. The identification of when maintenance activities occurred is reformulated as a model selection optimization problem based on Bayesian Information Criterion. An efficient solution algorithm utilizing adaptive thresholding and dynamic programming is proposed for solving this optimization problem. This framework’s validity and practicability are illustrated by the measurement data from the CIT inspection of the mileage section of K21 + 184 to K220 + 308 on the Nanchang-Fuzhou railway track from 2014 to 2019. The results indicate that this framework can overcome the disturbance of contaminated measurement data and accurately estimate when maintenance activities were undertaken without any historical maintenance records. What is more, the adaptive piecewise fitting model provided by this framework can describe the irregular deterioration process of corresponding rail track sections.
Highlights
Track irregularity directly impacts the running stability and safety of trains
A rail track deterioration modeling framework driven by historical measurement data from CIT is proposed
The proposed framework formulates the identification of maintenance activities with a model selection optimization problem, based on a modified Bayesian Information Criterion by incorporating an optimized weight for the model complexity component into the objective function
Summary
Track irregularity directly impacts the running stability and safety of trains. Maintaining tracks in an acceptable condition is essential, but it consumes many physical and staff resources. For 1 ≤ r ≤ m~ , the set of optimal results with a different number of selected candidate breakpoints is denoted by f (τ0) [f1(τ0), f2(τ0), /fm~ (τ0)] and FIGURE 5 | The identified maintenance-points and piecewise fitting model of section one: (A) the candidate breakpoints identified by the adaptive thresholding method; (B) the value of BIC for a different number of selected candidate breakpoints; (C) the piecewise fitting model. Relying on the historical measurement data from 2014 to 2019 for the nearly 200 kmlong track sections of the Nanchang-Fuzhou rail line (as shown in Figure 4), we obtain the optimal value of ζ which enables the identified maintenance-points to almost correspond with the actual ones. It indicates that this framework is capable of overcoming the disturbance of contaminated measurement data and accurately distinguishing the maintenance-points from outliers within candidate breakpoints. We believe that the deterioration rates might be affected by maintenance activities
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