Theses and Dissertations

Date of Award

12-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Dr. Constantine Tarawneh

Second Advisor

Dr. Heinrich Foltz

Third Advisor

Dr. Arturo Fuentes

Abstract

An algorithm that utilizes vibration measurements was developed by the UTRGV Center for Railway Safety to monitor the condition of railroad bearings. This algorithm uses the data collected from accelerometers on the bearing adapters to determine if there is a defect, where the defect is within the bearing, and the approximate size of the defect. Laboratory testing was performed on the UTCRS single bearing test rig. A four-second sample window of the recorded vibration data is used by the algorithm to reliably identify the defective component inside the bearing with up to a 100% confidence level. However, considerable computational power is used to analyze the 20,480 data points. Consequently, if this condition monitoring algorithm is to be implemented on a wireless module, the battery life becomes restricted. Reducing the sample window to one second of data collected would conserve energy but might sacrifice some accuracy in the analysis. To that end, a wireless onboard condition monitoring module that collects one second of vibration data (5,120 data points) was fabricated and tested to compare its efficacy against the existing wired setup. The study presented here demonstrates that the optimized algorithm for the wireless system can reliably identify the bearing condition with negligible compromise to accuracy and lower power consumption.

Comments

Copyright 2020 Jonas Regan Leano Cuanang. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/optimizing-railroad-bearing-condition-monitoring/docview/2560065304/se-2?accountid=7119

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