Artificial intelligence aided track degradation prediction
Creators: name: Wang, Xin
Publisher: name: No Publisher Supplied
Year: 2024

description: Track geometry defects and broken rails are among primary causes of freight-train derailments in the United States. Track degradation modeling and prediction can be used to support preventive maintenance by identifying and prioritizing track segments that are more prone to track defects, thus reducing accidents and service disruptions.This dissertation develops a machine learning approach to predict foot-by-foot track geometry change accounting for spatial and temporal dependencies of track geometry change. First, a robust position synchronization method is proposed to geospatially align track geometry data from multiple inspections accounting for positional errors, which provides high-quality geometry data for track condition evaluation and prediction. Subsequently, based on the processed data, a spatial-temporal model, CNN-LSTM, is proposed for foot-by-foot track geometry prediction by effectively capturing spatial and temporal information. A case study has been conducted using data from one Class I railroad. It shows that the positional error correction method can reduce the relative positional error to less than 1 foot with a 99% confidence interval. The proposed CNN-LSTM model outperforms four alternative models, including a naïve model (using the last observation), multi-layer perceptron (MLP), plain CNN, and plain LSTM, for both short-term and long-term prediction periods.Additionally, this dissertation adopts an ensemble learning framework to predict fatigue-related broken rail by extracting features from the multi-source databases related to infrastructure, operation, inspection, and weather conditions. Three data treatment methods (i.e., no resampling, undersampling, and oversampling) are incorporated into the proposed models, which include logistic regression, random forests, and gradient boosting. The findings show that gradient boosting (specifically XGBoost) with undersampling approach outperforms other machine learning methods for the studied dataset. It also identifies features that have a significant influence on broken rail prediction, such as the ratio of annual traffic density to rail weight, days since last broken rail, and days since last rail defect. Using this model, when 10% of the railroad network is scanned, approximately 33% of broken rails can be captured using the proposed methods. Finally, a spatial-temporal neural network model based on ResNet-Transformer architecture is developed for broken rail prediction to consider both spatial and temporal dependencies. Utilizing the ResNet architecture, the proposed model captures detailed spatial correlations among rail characteristics. Subsequently, the Transformer architecture is utilized to effectively handle long-term temporal data patterns and dependencies that reflect dynamic changes over time. Experiment results demonstrate that ResNet-Transformer model significantly outperforms the traditional machine learning approach, especially in identifying segments that might be more prone to broken rails. When screening 10% of the railroad network, the model can capture approximately 42% of broken rails. It indicates the effectiveness of the proposed ResNet-Transformer framework for broken rail prediction. The proposed artificial intelligence aided framework can aid in proactive maintenance and strategic capital planning in the railroad industry.