Dynamic-Time-Warping-Based Measurement Data Alignment Model for Condition-Based Railroad Track Maintenance

Abstract

Condition-based maintenance is believed to be a cost-effective and safety-assured strategy for railroad track management. Implementation of the strategy strongly relies on reliable and complete track condition data, reliable track deterioration models, and efficient and solvable mathematical models for optimal track maintenance scheduling. In practice, reliability of track condition inspection data is often in question; therefore, collected inspection data need to be preprocessed before it is used to implement a condition-based maintenance strategy. Reliable track condition inspection data means accurate positioning data and noiseless condition parameter measurements. Based on dynamic time warping, which is a widely used technique in the area of speech signal processing and biomedical engineering, this paper presents a robust optimization model for correcting positional errors of inspection data from a track geometry car, which is a kind of specialized instrument that is extensively used to measure the condition of tracks under wheel loadings. An efficient solution algorithm for the model is proposed as well. Applications of the model to inspection data from the track geometry car show that positional errors are almost removed from the inspection data, regardless of noises in condition parameter measurements and track maintenance interventions, and the model takes 1.5004 s, on average, to complete the positional error correction for a 1-km-long track segment. The presented model is adjustable to alignment of data sequences in many other areas, e.g., railroad inspection by track geometry trolley, highway roughness inspection by Light Detection and Ranging (LiDAR) vehicles, and railroad catenary wire geometry inspection.