Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors
Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empiri...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/908140 |
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| author | David Camarena-Martinez Martin Valtierra-Rodriguez Arturo Garcia-Perez Roque Alfredo Osornio-Rios Rene de Jesus Romero-Troncoso |
| author_facet | David Camarena-Martinez Martin Valtierra-Rodriguez Arturo Garcia-Perez Roque Alfredo Osornio-Rios Rene de Jesus Romero-Troncoso |
| author_sort | David Camarena-Martinez |
| collection | DOAJ |
| description | Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. |
| format | Article |
| id | doaj-art-582afdace9354046bdd9fe28ebc8cd4d |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-582afdace9354046bdd9fe28ebc8cd4d2025-02-03T01:06:52ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/908140908140Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction MotorsDavid Camarena-Martinez0Martin Valtierra-Rodriguez1Arturo Garcia-Perez2Roque Alfredo Osornio-Rios3Rene de Jesus Romero-Troncoso4HSPdigital-CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, 76807 San Juan del Río, QRO, MexicoHSPdigital-CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, 76807 San Juan del Río, QRO, MexicoHSPdigital-CA Telematica, Procesamiento Digital de Señales, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5 + 1.8, Palo Blanco, 36700 Salamanca, GTO, MexicoHSPdigital-CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, 76807 San Juan del Río, QRO, MexicoHSPdigital-CA Telematica, Procesamiento Digital de Señales, DICIS, Universidad de Guanajuato, Carr. Salamanca-Valle km 3.5 + 1.8, Palo Blanco, 36700 Salamanca, GTO, MexicoNowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.http://dx.doi.org/10.1155/2014/908140 |
| spellingShingle | David Camarena-Martinez Martin Valtierra-Rodriguez Arturo Garcia-Perez Roque Alfredo Osornio-Rios Rene de Jesus Romero-Troncoso Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors The Scientific World Journal |
| title | Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors |
| title_full | Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors |
| title_fullStr | Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors |
| title_full_unstemmed | Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors |
| title_short | Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors |
| title_sort | empirical mode decomposition and neural networks on fpga for fault diagnosis in induction motors |
| url | http://dx.doi.org/10.1155/2014/908140 |
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