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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

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Author(s):
Dias, Cleber Gustavo [1] ; da Silva, Luiz Carlos [1] ; Chabu, Ivan Eduardo [2]
Total Authors: 3
Affiliation:
[1] Nove Julho Univ UNINOVE, Informat & Knowledge Management Grad Program PPGI, Rua Vergueiro, Liberdade 235-249, BR-01504001 Sao Paulo - Brazil
[2] Univ Sao Paulo, Dept Elect Automat & Energy Engn, BR-05508070 Sao Paulo - Brazil
Total Affiliations: 2
Document type: Journal article
Source: ENERGIES; v. 12, n. 12 JUN 2 2019.
Web of Science Citations: 0
Abstract

This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false negative indications are more frequently. The efficiency of this approach has been proven using some computational simulation results and experimental tests to detect fully broken rotor bars in a 7.5 kW squirrel cage induction machine fed by line and an inverter. (AU)

FAPESP's process: 16/02525-1 - Diagnosis of broken bars in squirrel cage induction motors using intelligent techniques
Grantee:Cleber Gustavo Dias
Support Opportunities: Regular Research Grants
FAPESP's process: 18/05214-2 - Intelligent system applied to load torque and efficiency estimation in three-phase induction motors
Grantee:Cleber Gustavo Dias
Support Opportunities: Regular Research Grants