Theses and Dissertations

Date of Award


Document Type


Degree Name

Master of Science (MS)


Mechanical Engineering

First Advisor

Dr. Constantine Tarawneh

Second Advisor

Dr. Mohamadhossein Noruzoliaee

Third Advisor

Dr. Fatemeh Nazari


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.


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