Bayesian Multivariate Track Geometry Degradation Modelling and its use in Condition-Based Inspection

TL;DR

This paper introduces a Bayesian multivariate model for track geometry degradation that considers multiple correlated indicators, improving inspection efficiency and safety in railway maintenance.

Contribution

It develops a novel hierarchical Bayesian multivariate Wiener model for track degradation, incorporating correlations and data limitations, with practical validation and application in inspection policy.

Findings

Model accurately predicts track degradation using real-world data.

Application reduces inspection runs while maintaining safety standards.

Provides a framework for condition-based inspection policies.

Abstract

Effective maintenance of railway infrastructure is crucial for safe and comfortable transportation. Among the various degradation modes, track geometry deformation due to repeated loading significantly impacts operational safety. Detecting and maintaining acceptable track geometry involves the use of track recording vehicles (TRVs) that inspect and record geometric parameters. This study aims to develop a novel track geometry degradation model that considers multiple indicators and their correlations, accounting for both imperfect manual and mechanized tamping. A multivariate Wiener model is formulated to capture the characteristics of track geometry degradation. To address data limitations, a hierarchical Bayesian approach with Markov Chain Monte Carlo (MCMC) simulation is employed. This research contributes to the analysis of a multivariate predictive model, which considers the correlation between the degradation rates of multiple indicators, providing insights for rail operators and new track-monitoring systems. The model's performance is validated through a real-world case study on a commuter track in Queensland, Australia, using actual data and independent test datasets. Additionally, the study demonstrates the application of the proposed multivariate degradation model in developing a condition-based inspection policy for track geometry, potentially reducing the number of TRV runs while maintaining abnormal detection levels and failure rates.