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Machine Learning-Based Predictive Methods for Polyphase Motor Condition Monitoring

thesis
posted on 29.07.2022, 18:14 authored by David Matthew LeClercDavid Matthew LeClerc

  This paper explored the application of three machine learning models focused on predictive motor maintenance. Logistic Regression, Sequential Minimal Optimization (SMO), and NaïveBayes models. A comparative analysis of these models illustrated that while each had an accuracy greater than 95% in this study, the Logistic Regression Model exhibited the most reliable operation.

History

Degree Type

Master of Science

Department

Engineering Technology

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Grant Richards

Additional Committee Member 2

Gaurav Nanda

Additional Committee Member 3

Kenneth Burbank