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

Cleber Gustavo Dias, Luiz Carlos da Silva and Ivan Eduardo Chabu
Additional contact information
Cleber Gustavo Dias: Informatics and Knowledge Management Graduate Program (PPGI), Nove de Julho University-UNINOVE, Rua Vergueiro, Liberdade 235/249, Sao Paulo 01504-001, Brazil
Luiz Carlos da Silva: Informatics and Knowledge Management Graduate Program (PPGI), Nove de Julho University-UNINOVE, Rua Vergueiro, Liberdade 235/249, Sao Paulo 01504-001, Brazil
Ivan Eduardo Chabu: Department of Electrical Automation and Energy Engineering, São Paulo University, São Paulo 05508-070, Brazil

Energies, 2019, vol. 12, issue 12, 1-29

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.

Keywords: broken rotor bars; induction motor; fuzzy logic; support vector machine; hall sensor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/12/2381/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/12/2381/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:12:p:2381-:d:241711

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().