A novel domain adaptation method for drive-by railway track monitoring using progressive distribution alignment based on label correction

This paper proposes a data-centric method for identifying geometric defects in railway tracks using acceleration data collected from high-speed trains. Unlike many existing drive-by monitoring approaches that rely on classical supervised learning models requiring extensive labeled data from every line, a novel framework based on unsupervised domain adaptation (UDA) is proposed. This framework transfers the geometric defects diagnosis model learned from one line (source domain) to a new line (target domain) without necessitating labeled data from the latter. Given the variability in operational conditions, a detection model trained on one known scenario cannot be directly applied to another. Thus, the framework learns features sensitive to geometric defects and invariant to different tracks using the progressive distribution alignment based on label correction (PDALC) algorithm. Input data comprises labeled time-domain features extracted from acceleration data of the source line and unlabeled data from the target line. Output predictions are health status (target domain labels) for each track zone of the target line. The framework is evaluated using a comprehensive dataset of field measurements from a high-speed train traversing four different lines of France’s high-speed rail network, representing four distinct domains. Comparative results across 12 cross-domain recognition tasks reveal that the UDA framework based on PDALC outperforms four other UDA algorithms: transfer component analysis, maximum mean and covariance discrepancy, learning via low-rank and sparse representation, and geodesic flow kernel algorithm. Compared to the basic method (a classical supervised learning model without UDA), the proposed framework achieves a 12% increase in defect detection accuracy. Furthermore, the paper investigates the impact of different sensor layouts, tuning parameters of PDALC, classification algorithms, and the number of features on the accuracy of the approach.