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


Condition-based mostly maintenance is believed to be a price-effective and safety-assured strategy for railroad track management. Implementation of the strategy strongly relies on reliable and complete track condition knowledge, reliable track deterioration models, and economical and solvable mathematical models for optimal track maintenance scheduling. In follow, reliability of track condition inspection knowledge is typically in question; therefore, collected inspection data would like to be preprocessed before it's used to implement a condition-based mostly maintenance strategy. Reliable track condition inspection knowledge means that correct positioning data and noiseless condition parameter measurements. Based mostly on dynamic time warping, that may be a widely used technique in the realm of speech signal processing and biomedical engineering, this paper presents a sturdy optimization model for correcting positional errors of inspection knowledge from a track geometry automobile, which could be a reasonably specialised instrument that's extensively used to live the condition of tracks under wheel loadings. An efficient resolution algorithm for the model is proposed as well. Applications of the model to inspection knowledge from the track geometry car show that positional errors are virtually far from the inspection information, no matter noises in condition parameter measurements and track maintenance interventions, and therefore the model takes one.5004 s, on average, to complete the positional error correction for a 1-km-long track phase. The presented model is adjustable to alignment of knowledge sequences in several alternative areas, e.g., railroad inspection by track geometry trolley, highway roughness inspection by Lightweight Detection and Ranging (LiDAR) vehicles, and railroad catenary wire geometry inspection.

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