Deep Bayesian survival analysis of rail useful lifetime

Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases...

Rail break prediction and cause analysis using imbalanced in-service train data

Timely detection and identification of rail breaks are crucial for safety and reliability of railway networks. This paper proposes a new deep learning-based approach using the daily monitoring data from in-service trains. A time-series generative adversarial network (TimeGAN) is employed to mitigate the problem of data imbalance and preserve the...

River, tide and morphology interaction in a macro-tidal estuary with active morphological evolutions

Understanding tidal dynamics in estuaries is essential for tidal predictions and assessments of sediment transport and associated morphological changes. Most studies on river-tide interaction ignored the influences of morphological evolutions under natural conditions such as the seasonal and interannual variations of river discharge. This...