Machine Learning-Based Predictive Methods for Polyphase Motor Condition Monitoring
Abstract
Modern high-performing distribution centers extensively use conveyor systems driven by polyphase motors to efficiently move large quantities of goods from and through their intake, sorting, storage, and packing system. The Overall Equipment Effectiveness (OEE) of a distribution center is heavily dependent on the reliable operation of conveyance systems, which present an ongoing maintenance challenge, with motor maintenance comprising the most significant proportion of maintenance activity. 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.
Degree
M.Sc.
Advisors
Richards, Purdue University.
Subject Area
Artificial intelligence|Computer science|Information Technology|Web Studies
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