Vibration-Based Algorithm for Diagnostic and Prognostic Condition Monitoring of Railroad Bearings and Wheels

Author

Jeffery Ray Pams, The University of Texas Rio Grande Valley

Date of Award

5-2025

Document Type

Thesis

Degree Name

Master of Science in Engineering (MSE)

Department

Mechanical Engineering

First Advisor

Constantine Tarawneh

Second Advisor

Arturo Fuentes

Third Advisor

Heinrich Foltz

Abstract

Bearing and wheel failure accounts for the majority of equipment-related train derailments on US railroads. Current wayside monitoring systems have been insufficient in the prevention of catastrophic derailments prompting the rail industry to explore the possible integration of onboard condition monitoring systems into their rail operations. The University Transportation Center for Railway Safety (UTCRS) in partnership with Hum Industrial Technology, Inc., has developed a wireless onboard system intended for the condition monitoring of railroad bearings and wheels. This thesis presents the development, evaluation, and implementation of an algorithm designed to be used in conjunction with these systems to provide railroad operators with diagnostic and prognostic estimates. Laboratory testing and field implementation results demonstrate the potential of this algorithm to accurately identify and diagnose defective bearings and wheels and provide remaining useful life estimates for bearings. This information can aid the rail industry in scheduling proactive maintenance cycles and mitigating unnecessary and expensive train stoppages.

Comments

Copyright 2025 Jeffery Ray Pams. https://proquest.com/docview/3240612181

Recommended Citation

Pams, J. R. (2025). Vibration-Based Algorithm for Diagnostic and Prognostic Condition Monitoring of Railroad Bearings and Wheels [Master's thesis, The University of Texas Rio Grande Valley]. ScholarWorks @ UTRGV. https://scholarworks.utrgv.edu/etd/1698