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
|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|
|Keywords:||Trouble codes in automobiles|
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