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    A machine learning framework for prediction of Diagnostic Trouble Codes in automobiles

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    Mohan_Kopuru_Thesis.pdf (1.311 Mb )
    Author
    Kopuru, Mohan
    Item Type
    Graduate Thesis
    Advisor
    Rahimi, Shahram
    Committee
    Falls, Terril
    Swan, 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.
    Degree
    Master of Science
    Major
    Computer Science
    College
    James Worth Bagley College of Engineering
    Department
    Department of Computer Science and Engineering
    URI
    https://hdl.handle.net/11668/16949
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    • Theses and Dissertations
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    Mississippi State University Libraries
    395 Hardy Rd
    P.O. Box 5408, Mississippi State, MS 39762-5408
    (662) 325-7668
    (662) 325-0011
    (662) 325-8183
    Contact repository admin Report a problem Terms of use Privacy policy Accessibility MSU Legal
     

     

    Mississippi State University Libraries
    395 Hardy Rd
    P.O. Box 5408, Mississippi State, MS 39762-5408
    (662) 325-7668
    (662) 325-0011
    (662) 325-8183
    Contact repository admin Report a problem Terms of use Privacy policy Accessibility MSU Legal