A machine learning framework for prediction of Diagnostic Trouble Codes in automobiles
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Author
Kopuru, Mohan
Item Type
Graduate ThesisAdvisor
Rahimi, ShahramCommittee
Falls, TerrilSwan, J. Edward II
Rahimi, Shahram
Metrics
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.