Dissertation/Thesis Abstract

A Machine Learning Framework for Prediction of Diagnostic Trouble Codes in Automobiles
by Kopuru, Mohan Sri Krishna, M.S., Mississippi State University, 2020, 97; 27837965
Abstract (Summary)

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

Indexing (document details)
Advisor: Rahimi, Shahram
Commitee: Falls, Terril C, Swan, Edward J
School: Mississippi State University
Department: Computer Science and Engineering
School Location: United States -- Mississippi
Source: MAI 81/11(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Computer science
Keywords: Trouble codes in automobiles
Publication Number: 27837965
ISBN: 9798643196952
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