🤖 AI Summary
Orbital geometry degradation poses significant safety risks; however, conventional univariate models fail to capture multivariate coupling effects and the impact of imperfect maintenance. This paper proposes a novel multivariate degradation model integrating gauge, alignment, and longitudinal level, embedding a multivariate Wiener process within a hierarchical Bayesian framework for the first time. The model explicitly characterizes inter-variable correlations in degradation rates and accommodates uncertainty quantification under both manual and mechanized tamping interventions. Detection decisions are optimized via Markov Chain Monte Carlo (MCMC) sampling and conditional probability inference. Empirical validation on a commuter rail line in Queensland, Australia, demonstrates that the model achieves ≥95% anomaly detection rate and ≤0.8% false alarm rate while reducing inspection frequency by 32%. This substantially enhances both the reliability and cost-efficiency of condition-based track maintenance.
📝 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.
