Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors

Wagner Fontes Godoy, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Ivan Nunes da Silva, Alessandro Goedtel and Rodrigo Henrique Cunha Palácios
Additional contact information
Wagner Fontes Godoy: Department of Electrical Engineering, Av. Alberto Carazzai, Federal Technological University of Paraná (UTFPR), 1640, Centro, Cornélio Procópio 86.300-000, PR, Brazil
Daniel Morinigo-Sotelo: Department of Electrical Engineering, Paseo del Cauce, 59, University of Valladolid (UVa), 47011 Valladolid, Spain
Oscar Duque-Perez: Department of Electrical Engineering, Paseo del Cauce, 59, University of Valladolid (UVa), 47011 Valladolid, Spain
Ivan Nunes da Silva: Department of Electrical Engineering, University of São Paulo (USP), São Carlos School of Engineering, Av. Trabalhador São Carlense, 400, Centro, São Carlos 13.566-590, SP, Brazil
Alessandro Goedtel: Department of Electrical Engineering, Av. Alberto Carazzai, Federal Technological University of Paraná (UTFPR), 1640, Centro, Cornélio Procópio 86.300-000, PR, Brazil
Rodrigo Henrique Cunha Palácios: Department of Electrical Engineering, Av. Alberto Carazzai, Federal Technological University of Paraná (UTFPR), 1640, Centro, Cornélio Procópio 86.300-000, PR, Brazil

Energies, 2020, vol. 13, issue 13, 1-17

Abstract: This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.

Keywords: three-phase induction motor; diagnosis; bearing faults; intelligent estimation (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/13/3481/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/13/3481/ (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:13:y:2020:i:13:p:3481-:d:380748

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 ().