Application of Online Automated Segmentation and Evaluation Method in Anomaly Detection at Rail Profile Based on Pattern Matching and Complex Networks

Abstract

In steel rail production, complex deformations can induce non-uniform changes in cross-sectional profiles along the rail's length, resulting in unevenness and safety implications. It is essential to perform dimensional testing to ascertain compliance with standard requirements. Currently, profile inspection results are manually evaluated, posing efficiency challenges and a lack of standardized criteria.To address this challenge, this paper proposes an online automatic steel rail segmentation and evaluation method (online-ASE) based on pattern matching and complex networks to enable automatic rail profile assessment. This method initially utilizes offline high-dimensional time series data for conducting Toeplitz Inverse Covariance-based Clustering (TICC) training and constructs a standard quality characterization pattern library through distinct inverse covariance structures between abnormal and normal high-dimensional quality characterization indicators of steel rails. When applied online, the Viterbi shortest path dynamic programming algorithm is utilized to match steel rail data with the pattern library, swiftly identifying anomalous rail segments. Additionally, the algorithm computes the contribution of steel rail quality parameters to the segmentation results using complex network betweenness centrality, thereby explaining the reasons for segment formation. These explanations provide a reference basis for subsequent steel rail repairs. Finally, the effectiveness of the proposed method is validated using real-world steel rail data from a specific steel factory in China.