Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Martin Valtierra-Rodriguez, Juan Pablo Amezquita-Sanchez, Arturo Garcia-Perez and David Camarena-Martinez
Additional contact information
Martin Valtierra-Rodriguez: ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro (UAQ), Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro 76807, Mexico
Juan Pablo Amezquita-Sanchez: ENAP-Research Group, CA-Sistemas Dinámicos, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro (UAQ), Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro 76807, Mexico
Arturo Garcia-Perez: CA Procesamiento Digital de Señales, Departamento de Electrónica, División de Ingenierías Campus Irapuato-Salamanca (DICIS), Salamanca, Guanajuato 36885, Mexico
David Camarena-Martinez: CA Procesamiento Digital de Señales, Departamento de Electrónica, División de Ingenierías Campus Irapuato-Salamanca (DICIS), Salamanca, Guanajuato 36885, Mexico

Mathematics, 2019, vol. 7, issue 9, 1-19

Abstract: Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

Keywords: broken rotor bar; CEEMD; condition monitoring; FPGA; induction motor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/7/9/783/pdf (application/pdf)
https://www.mdpi.com/2227-7390/7/9/783/ (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:jmathe:v:7:y:2019:i:9:p:783-:d:260747

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

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