A Comparison of Machine Learning Methods to Identify Broken Bar Failures in Induction Motors Using Statistical Moments (ISDA 2017)

A Comparison of Machine Learning Methods to Identify Broken Bar Failures in Induction Motors Using Statistical Moments (ISDA 2017)

A Comparison of Machine Learning Methods to Identify Broken Bar Failures in Induction Motors Using Statistical Moments published at Intelligent Systems Design and Applications ( ISDA 2017).

DOI: https://doi.org/10.1007/978-3-319-76348-4_13

Abstract: Induction motors are reported as the horse power in industries. Due to its importance, researchers studied how to predict its faults in order to improve reliability. Condition health monitoring plays an important role in this field, since it is possible to predict failures by analyzing its operational data. This paper proposes the usage of vibration signals, combined with Higher-Order Statistics (HOS) and machine learning methods to detect broken bars in a squirrel-cage three-phase induction motor. The Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), Optimum-Path Forest and Naive-Bayes were used and have achieved promising results: high classification rate with SVM, high sensitivity rate with MLP and fast training convergence with OPF.

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