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Vibration-Based Machine Learning Models For Condition Monitoring Of Railroad Rolling Stock, Sergio M. Martinez
Vibration-Based Machine Learning Models For Condition Monitoring Of Railroad Rolling Stock, Sergio M. Martinez
Theses and Dissertations
One of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict …