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

  • Navar de Medeiros Mendonça e Nascimento
  • Cláudio Marques de Sá Medeiros
  • Pedro Pedrosa Rebouças Filho
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

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.

Keywords

Higher-Order Statistics Induction motor Patter recognition Vibration signal 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Navar de Medeiros Mendonça e Nascimento
    • 1
  • Cláudio Marques de Sá Medeiros
    • 1
  • Pedro Pedrosa Rebouças Filho
    • 1
  1. 1.Programa de Pós-Graduao em Energias RenováveisInstituto Federal de Educação, Ciência e Tecnologia do CearáFortalezaBrazil

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