Diagnosis of broken bars in squirrel cage induction motors using intelligent techniques

• Grant number: 16/02525-1
• Support type: Regular Research Grants
• Duration: June 01, 2016 - May 31, 2018
• Field of knowledge: Engineering - Electrical Engineering - Electrical, Magnetic and Electronic Measurements, Instrumentation
• Principal researcher: Cleber Gustavo Dias
• Grantee: Cleber Gustavo Dias
• Home Institution: Universidade Nove de Julho (UNINOVE). Campus Memorial. São Paulo , SP, Brazil
• Assoc. researchers: Fabio Henrique Pereira

Abstract

Three phase induction motors, particularly those with a squirrel cage rotor, are widely used in Brazil and around the world, as a main driving force for industrial machines, among others. A relevant fault is associated with an improper operation of these motors, such as the broken bars that is related to the rotor cage in the connection with the end-ring. Over the past decades, many works dedicated to study and to development new techniques capable of identifying and diagnosing faults in the rotor cage at early stages. However, the state of the art solutions, as the motor current signature analysis, still have significant limitations for some conditions, such as operating at low slip and/or the presence of non-adjacent broken bars. In this context, the present work proposes an invasive and intelligent approach, capable of identifying and diagnosing one and more breakage bars, adjacent and/or non-adjacent, in squirrel cage induction motors, by combining artificial neural networks and fuzzy logic, with at least two methods using digital signal processing, as Wavelets Transform and Fast Fourier Transform. A Hall Effect Sensor should be installed inside the machine for later analysis of potential disturbances in the air-gap flux, when the defect occurrence. Thus, the resulting magnetic flux disturbances in the Hall Sensor should be evaluated, in order to detect faults in the rotor bars. The validation of the present work will be performed from computational simulation results, using Simulink/Matlab® software, as well the results obtained from experimental tests, considering a system with motor and a dynamic magnetic brake (Foucault). (AU)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DIAS, CLEBER GUSTAVO; DA SILVA, LUIZ CARLOS; LUZ ALVES, WONDER ALEXANDRE. A Histogram of Oriented Gradients Approach for Detecting Broken Bars in Squirrel-Cage Induction Motors. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, v. 69, n. 9, p. 6968-6981, SEPT 2020. (16/02547-5, 16/02525-1, 18/05214-2)

DIAS, CLEBER GUSTAVO; DA SILVA, LUIZ CARLOS; CHABU, IVAN EDUARDO. Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors. ENERGIES, v. 12, n. 12, JUN 2 2019. (16/02525-1, 18/05214-2)

DIAS, CLEBER GUSTAVO; DE SOUSA, CRISTIANO MORAIS. A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction Motors at Very Low Slip. JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS, v. 29, n. 4, p. 489-499, AUG 2018. (16/02525-1)

DIAS, CLEBER GUSTAVO; PEREIRA, FABIO HENRIQUE. Broken Rotor Bars Detection in Induction Motors Running at Very Low Slip Using a Hall Effect Sensor. IEEE SENSORS JOURNAL, v. 18, n. 11, p. 4602-4613, JUN 1 2018. (16/02525-1)