Theses and Dissertations

Date of Award

5-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Dr. Constantine Tarawneh

Second Advisor

Dr. Arturo Fuentes

Third Advisor

Dr. Robert Jones

Abstract

There are a few different bearing health monitoring technologies currently used in the railroad industry, both reactive and preventative detection systems. Reactive models have proven to be ineffective in monitoring bearing health, which has resulted in either unnecessary train stoppages and delays or in-service failures and bearing burn-off leading to catastrophic train derailments. Wayside preventative detection systems, while more effective than reactive technologies, are scarce and neglect railcars’ that do not travel over a specific route. This knowledge prompted the University Transportation Center for Railway Safety at UTRGV to develop an onboard bearing health monitoring system that can accurately assess the health of a bearing and identify the defective component at an early stage of the defect development. This system has been proven to accurately detect defective bearings through extensive laboratory testing validated by field testing performed at the Transportation Technology Center, Inc. in Pueblo, CO. Using this system, a prognostic model for the residual service life of a defective bearing was developed. This model can be used by the railroads to schedule proactive maintenance cycles to mitigate inefficient faulty bearing replacements.

Comments

Copyright 2019 Jennifer Danni Lima. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/residual-service-life-prognostic-models-tapered/docview/2452444635/se-2?accountid=7119

Download

Share

COinS