<p> 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.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/20402718 |
| Date | 29 July 2022 |
| Creators | David Matthew LeClerc (13048125) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Machine_Learning-Based_Predictive_Methods_for_Polyphase_Motor_Condition_Monitoring/20402718 |
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