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Dissertation/Thesis Abstract
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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
| 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 |
