Author

Mohan Kopuru

Advisor

Rahimi, Shahram

Committee Member

Falls, Terril

Committee Member

Swan, J. Edward II

Committee Member

Rahimi, Shahram

Date of Degree

5-1-2020

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Predictive Maintenance is an important solution to the rising maintenance costs in the industries. With the advent of intelligent computer and availability of data, predictive maintenance is seen as a solution to predict and prevent the occurrence of the faults in the different types of machines. This thesis provides a detailed methodology to predict the occurrence of critical Diagnostic Trouble codes that are observed in a vehicle in order to take necessary maintenance actions before occurrence of the fault in automobiles using Convolutional Neural Network architecture.

URI

https://hdl.handle.net/11668/16949

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