Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors
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...
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doaj-47b38aa9f1ec4654ac5691554779b8da2020-11-25T02:45:29ZengMDPI AGMathematics2227-73902019-08-017978310.3390/math7090783math7090783Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction MotorsMartin Valtierra-Rodriguez0Juan Pablo Amezquita-Sanchez1Arturo Garcia-Perez2David Camarena-Martinez3ENAP-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, MexicoENAP-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, MexicoCA Procesamiento Digital de Señales, Departamento de Electrónica, División de Ingenierías Campus Irapuato-Salamanca (DICIS), Salamanca, Guanajuato 36885, MexicoCA Procesamiento Digital de Señales, Departamento de Electrónica, División de Ingenierías Campus Irapuato-Salamanca (DICIS), Salamanca, Guanajuato 36885, MexicoEmpirical 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.https://www.mdpi.com/2227-7390/7/9/783broken rotor barCEEMDcondition monitoringFPGAinduction motor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Martin Valtierra-Rodriguez Juan Pablo Amezquita-Sanchez Arturo Garcia-Perez David Camarena-Martinez |
spellingShingle |
Martin Valtierra-Rodriguez Juan Pablo Amezquita-Sanchez Arturo Garcia-Perez David Camarena-Martinez Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors Mathematics broken rotor bar CEEMD condition monitoring FPGA induction motor |
author_facet |
Martin Valtierra-Rodriguez Juan Pablo Amezquita-Sanchez Arturo Garcia-Perez David Camarena-Martinez |
author_sort |
Martin Valtierra-Rodriguez |
title |
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors |
title_short |
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors |
title_full |
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors |
title_fullStr |
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors |
title_full_unstemmed |
Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors |
title_sort |
complete ensemble empirical mode decomposition on fpga for condition monitoring of broken bars in induction motors |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2019-08-01 |
description |
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. |
topic |
broken rotor bar CEEMD condition monitoring FPGA induction motor |
url |
https://www.mdpi.com/2227-7390/7/9/783 |
work_keys_str_mv |
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