Predicting the Remaining Service Life of Railroad Bearings: Leveraging Machine Learning and Onboard Sensor Data

Author

Leonel Villafranca, The University of Texas Rio Grande Valley

Date of Award

7-2022

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Dr. Constantine Tarawneh

Second Advisor

Dr. Mohamadhossein Noruzoliaee

Third Advisor

Dr. Fatemeh Nazari

Abstract

By continuously monitoring train bearing health in terms of temperature and vibration levels of bearings tested in a laboratory setting, statistical regression models have been developed to establish relationships between the sensor-acquired bearing health data with several explanatory factors that potentially influence the bearing deterioration. Despite their merits, statistical models fall short of reliable prediction accuracy levels since they entail restrictive assumptions, such as a priori known functional relationship between the response and input variables. A data-driven machine learning algorithm is presented, which can unravel the nonlinear deterioration model purely based on the bearing health data, even when the structure is not apparent. More specifically, a Gradient Boosting Machine is trained using vast amounts of laboratory data collected over the course of over a decade. This will help predict bearing failure, thus, providing railroads and railcar owners the opportunity to schedule preventive maintenance cycles rather than costly reactive ones.

Comments

Copyright 2022 Leonel Villafranca. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/predicting-remaining-service-life-railroad/docview/2739757613/se-2?accountid=7119

Recommended Citation

Villafranca, Leonel, "Predicting the Remaining Service Life of Railroad Bearings: Leveraging Machine Learning and Onboard Sensor Data" (2022). Theses and Dissertations. 1111.
https://scholarworks.utrgv.edu/etd/1111