Theses and Dissertations - UTB/UTPA

Date of Award


Document Type


Degree Name

Master of Science (MS)


Mechanical Engineering

First Advisor

Dr. Constantine M. Tarawneh

Second Advisor

Dr. Arturo Fuentes

Third Advisor

Dr. Robert Jones


Of the three main rail accident types in the United States, derailments are the most common. Studies have shown that derailments occurring above 25 mph are caused by equipment failures such as bearing failures. As a preventative measure, condition-monitoring systems are required to detect defective bearings in the field. Based on data compiled from multiple experiments of healthy and defective bearings at UTPA, an algorithm has been devised to identify bearing abnormalities. Vibration analysis techniques are utilized to potentially determine whether a bearing is defective and the defect type and size using the acquired vibration signatures. Implementation of a defect detection algorithm in the field can prevent catastrophic bearing failures from occurring; thus, reducing the chances of costly train derailments. This thesis summarizes the work done to develop an effective vibration-based defect detection algorithm that has been validated through field testing.


Copyright 2015 Amy Gonzalez. All Rights Reserved.

Granting Institution

University of Texas-Pan American