Stray Flux Analysis for the Detection and Severity Categorization of Rotor Failures in Induction Machines Driven by Soft-Starters

Vicente Biot-Monterde, Ángela Navarro-Navarro, Jose A. Antonino-Daviu and Hubert Razik
Additional contact information
Vicente Biot-Monterde: Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Cno. de Vera, S/N, 46022 Valencia, Spain
Ángela Navarro-Navarro: Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Cno. de Vera, S/N, 46022 Valencia, Spain
Jose A. Antonino-Daviu: Departamento de Ingeniería Eléctrica, Universitat Politècnica de València, Cno. de Vera, S/N, 46022 Valencia, Spain
Hubert Razik: Laboratory Ampère UMR 5005, University of Lyon, 69007 Lyon, France

Energies, 2021, vol. 14, issue 18, 1-18

Abstract: The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) are one of the most commonly used. In many industrial applications, the IM are driven by different types of starters, quite often by soft-starters. Despite rotor damages are more prone to occur in line-started motors, these kind of failures have been also reported in those ones driven by soft-starters. Related to this, the use of these type of starters may introduce some harmonic components, that could veil the magnetic flux signature of the different rotor faults. So, the aim of this study is to confirm if the Stray Flux Analysis technique maintains its reliability in these cases. Thus, this article presents the results of soft-started induction motors start-up tests, both in healthy and faulty motors. The fault components are detected by analyzing the stray flux during the starting and the study is complemented by analyzing the stray flux during the steady-state. In addition to the failure patterns, numerical indicators have been found so the identification of the failures is not only qualitative, but also quantitative. The results confirm the potential of the technique for detecting electromechanical failures in soft-started SCIMs.

Keywords: induction machines; failure diagnosis; stray flux; signal processing; soft-starters (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/18/5757/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/18/5757/ (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:14:y:2021:i:18:p:5757-:d:634399

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 ().