Enhancing three-phase induction motor reliability with health index and artificial intelligence-driven predictive maintenance.

Journal: Royal Society open science
Published Date: May 28, 2025

Abstract

The aim of this work is to assist in the maintenance of three-phase induction motors by creating a health index for this equipment. The proposed approach is based on power quality concepts, the creation of an algebraic algorithm to determine the health index and the use of artificial intelligence algorithms for modelling time series, such as Autoregressive Integrated Moving Average and Facebook Prophet, to predict the future health of the motor based on its historical data. The use of historical data makes it possible to anticipate potential failures and guide predictive maintenance strategies, helping to reduce costs and minimize unplanned downtime. The study examines various causes of failure in three-phase induction motors, analysing some of the most recurrent failures, their implications and the resulting impacts on the performance of the three-phase induction motor.

Authors

  • Felipe Lima Aires
    Faculty of Science and Technology, Federal University of Goias, Aparecida de Goiania, Goias, Brazil.
  • Gabriel Dias Galeno
    School of Electrical, Mechanical and Computer Engineering, Federal University of Goias, Goiania, Goias, Brazil.
  • Fernando Nunes Belchior
    Faculty of Science and Technology, Federal University of Goias, Aparecida de Goiania, Goias, Brazil.
  • Antonio Melo Oliveira
    School of Electrical, Mechanical and Computer Engineering, Federal University of Goias, Goiania, Goias, Brazil.
  • Julian David Hunt
    Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

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