Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection

Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant...
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Autor*in:	Victor Avina-Corral [verfasserIn] Jose de Jesus Rangel-Magdaleno [verfasserIn] Hayde Peregrina-Barreto [verfasserIn] Juan Manuel Ramirez-Cortes [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 24181-24193
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:24181-24193

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3154410

Katalog-ID:	DOAJ028215656

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allfields	10.1109/ACCESS.2022.3154410 doi (DE-627)DOAJ028215656 (DE-599)DOAJ26e938c559b84eddbffcb59a37fb1fca DE-627 ger DE-627 rakwb eng TK1-9971 Victor Avina-Corral verfasserin aut Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions. Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform Electrical engineering. Electronics. Nuclear engineering Jose de Jesus Rangel-Magdaleno verfasserin aut Hayde Peregrina-Barreto verfasserin aut Juan Manuel Ramirez-Cortes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 24181-24193 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:24181-24193 https://doi.org/10.1109/ACCESS.2022.3154410 kostenfrei https://doaj.org/article/26e938c559b84eddbffcb59a37fb1fca kostenfrei https://ieeexplore.ieee.org/document/9721248/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 24181-24193
spelling	10.1109/ACCESS.2022.3154410 doi (DE-627)DOAJ028215656 (DE-599)DOAJ26e938c559b84eddbffcb59a37fb1fca DE-627 ger DE-627 rakwb eng TK1-9971 Victor Avina-Corral verfasserin aut Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions. Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform Electrical engineering. Electronics. Nuclear engineering Jose de Jesus Rangel-Magdaleno verfasserin aut Hayde Peregrina-Barreto verfasserin aut Juan Manuel Ramirez-Cortes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 24181-24193 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:24181-24193 https://doi.org/10.1109/ACCESS.2022.3154410 kostenfrei https://doaj.org/article/26e938c559b84eddbffcb59a37fb1fca kostenfrei https://ieeexplore.ieee.org/document/9721248/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 24181-24193
allfields_unstemmed	10.1109/ACCESS.2022.3154410 doi (DE-627)DOAJ028215656 (DE-599)DOAJ26e938c559b84eddbffcb59a37fb1fca DE-627 ger DE-627 rakwb eng TK1-9971 Victor Avina-Corral verfasserin aut Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions. Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform Electrical engineering. Electronics. Nuclear engineering Jose de Jesus Rangel-Magdaleno verfasserin aut Hayde Peregrina-Barreto verfasserin aut Juan Manuel Ramirez-Cortes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 24181-24193 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:24181-24193 https://doi.org/10.1109/ACCESS.2022.3154410 kostenfrei https://doaj.org/article/26e938c559b84eddbffcb59a37fb1fca kostenfrei https://ieeexplore.ieee.org/document/9721248/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 24181-24193
allfieldsGer	10.1109/ACCESS.2022.3154410 doi (DE-627)DOAJ028215656 (DE-599)DOAJ26e938c559b84eddbffcb59a37fb1fca DE-627 ger DE-627 rakwb eng TK1-9971 Victor Avina-Corral verfasserin aut Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions. Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform Electrical engineering. Electronics. Nuclear engineering Jose de Jesus Rangel-Magdaleno verfasserin aut Hayde Peregrina-Barreto verfasserin aut Juan Manuel Ramirez-Cortes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 24181-24193 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:24181-24193 https://doi.org/10.1109/ACCESS.2022.3154410 kostenfrei https://doaj.org/article/26e938c559b84eddbffcb59a37fb1fca kostenfrei https://ieeexplore.ieee.org/document/9721248/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 24181-24193
allfieldsSound	10.1109/ACCESS.2022.3154410 doi (DE-627)DOAJ028215656 (DE-599)DOAJ26e938c559b84eddbffcb59a37fb1fca DE-627 ger DE-627 rakwb eng TK1-9971 Victor Avina-Corral verfasserin aut Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions. Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform Electrical engineering. Electronics. Nuclear engineering Jose de Jesus Rangel-Magdaleno verfasserin aut Hayde Peregrina-Barreto verfasserin aut Juan Manuel Ramirez-Cortes verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 24181-24193 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:24181-24193 https://doi.org/10.1109/ACCESS.2022.3154410 kostenfrei https://doaj.org/article/26e938c559b84eddbffcb59a37fb1fca kostenfrei https://ieeexplore.ieee.org/document/9721248/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 24181-24193
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callnumber-first	T - Technology
author	Victor Avina-Corral
spellingShingle	Victor Avina-Corral misc TK1-9971 misc Bearing fault detection (BFD) misc edge detection (ED) misc induction machine (IM) misc maximal overlap discrete wavelet transform misc Electrical engineering. Electronics. Nuclear engineering Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection
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topic_title	TK1-9971 Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection Bearing fault detection (BFD) edge detection (ED) induction machine (IM) maximal overlap discrete wavelet transform
topic	misc TK1-9971 misc Bearing fault detection (BFD) misc edge detection (ED) misc induction machine (IM) misc maximal overlap discrete wavelet transform misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Bearing fault detection (BFD) misc edge detection (ED) misc induction machine (IM) misc maximal overlap discrete wavelet transform misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Bearing fault detection (BFD) misc edge detection (ED) misc induction machine (IM) misc maximal overlap discrete wavelet transform misc Electrical engineering. Electronics. Nuclear engineering
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title	Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection
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title_full	Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection
author_sort	Victor Avina-Corral
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title_sort	bearing fault detection in asd-powered induction machine using modwt and image edge detection
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title_auth	Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection
abstract	Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions.
abstractGer	Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions.
abstract_unstemmed	Today the industry depends on various types of three-phase induction machines, requiring operating at variable speeds to perform more complex processes. Therefore, it is vital to monitor their operation conditions to maintain the optimal efficiency of the processes they perform and avoid significant economic losses. The proposed work presents the design and development of a method for bearing damage detection based on Maximal Overlap Discrete Wavelet Transform and image processing for edge detection. Accuracies achieved with three types of damage exceed 90%. The signals for the test are acquired from seven different operating conditions for each type of damage. Supply comes from a power grid source and an adjustable speed drive. The Maximal Overlap Discrete Wavelet Transform is applied with different filtering levels to the three phases of the stator current, the magnitude of the filtered signals is acquired, a periodic two-dimensional array is generated and further smoothed by a Gaussian filter allowing the observation of patterns at the edges. Finally, the obtained images are scanned with a 2-D mask aiming to detect and count patterns associated to the fault detection process. Statistical analysis is performed over characteristic signatures obtained from the current magnitude of the three phases at different classes of damage and several mechanical load conditions.
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title_short	Bearing Fault Detection in ASD-Powered Induction Machine Using MODWT and Image Edge Detection
url	https://doi.org/10.1109/ACCESS.2022.3154410 https://doaj.org/article/26e938c559b84eddbffcb59a37fb1fca https://ieeexplore.ieee.org/document/9721248/ https://doaj.org/toc/2169-3536
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up_date	2024-07-03T16:22:27.119Z
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