Fractional Calculus-Based Processing for Feature Extraction in Harmonic-Polluted Fault Monitoring Systems

Nathaly Murcia-Sepúlveda, Jorge M. Cruz-Duarte, Ignacio Martin-Diaz, Arturo Garcia-Perez, J. Juan Rosales-García, Juan Gabriel Avina-Cervantes and Carlos Rodrigo Correa-Cely
Additional contact information
Nathaly Murcia-Sepúlveda: División de Ingenierías del Campus Irapuato-Salamanca, Universidad de Guanajuato, Salamanca, Guanajuato 36885, Mexico
Jorge M. Cruz-Duarte: Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey, Nuevo León 64849, Mexico
Ignacio Martin-Diaz: Polytechnic School, Universidad Europea Miguel de Cervantes, 47012 Valladolid, Spain
Arturo Garcia-Perez: División de Ingenierías del Campus Irapuato-Salamanca, Universidad de Guanajuato, Salamanca, Guanajuato 36885, Mexico
J. Juan Rosales-García: División de Ingenierías del Campus Irapuato-Salamanca, Universidad de Guanajuato, Salamanca, Guanajuato 36885, Mexico
Juan Gabriel Avina-Cervantes: División de Ingenierías del Campus Irapuato-Salamanca, Universidad de Guanajuato, Salamanca, Guanajuato 36885, Mexico
Carlos Rodrigo Correa-Cely: Escuela de Ingenierías Eléctrica, Electrónica y de Telecomunicaciones, Universidad Industrial de Santander, Bucaramanga, Santander 680002, Colombia

Energies, 2019, vol. 12, issue 19, 1-14

Abstract: Fault monitoring systems in Induction Motors (IMs) are in high demand since many production environments require yielding detection tools independent of their power supply. When IMs are inverter-fed, they become more complicated to diagnose via spectral techniques because those are susceptible to produce false positives. This paper proposes an innovative and reliable methodology to ease the monitoring and fault diagnosis of IMs. It employs fractional Gaussian windows determined from Caputo operators to stand out from spectral harmonic trajectories. This methodology was implemented and simulated to process real signals from an induction motor, in both healthy and faulty conditions. Results show that the proposed technique outperforms several traditional approaches by getting the clearest and most useful patterns for feature extraction purposes.

Keywords: motor fault detection; fractional calculus; time-frequency; harmonic-based feature extraction (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 complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/19/3736/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/19/3736/ (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:19:p:3736-:d:272237

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 ().