Early Detection of Faults in Induction Motors
In modern industries, induction motors are the backbone of numerous applications, powering everything from manufacturing facilities to transportation systems. While they are known for their reliability, unexpected failures can still occur, leading to increased operational costs, facility damage, or...
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| Format: | Online |
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| Sprache: | Englisch |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Online-Zugang: | ONIX_20231130_9783036593357_299 |
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| collection | Directory of Open Access Books |
| description | In modern industries, induction motors are the backbone of numerous applications, powering everything from manufacturing facilities to transportation systems. While they are known for their reliability, unexpected failures can still occur, leading to increased operational costs, facility damage, or service interruptions. "Early Detection and Fault Diagnosis of Induction Motors" is a comprehensive volume that compiles ten innovative journal articles focused on maintaining these machines. The papers explore a variety of techniques that introduce new ideas to the field. |
| format | Online |
| id | doab-20.500.12854ir-128847 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1288472024-04-11T15:10:16Z Early Detection of Faults in Induction Motors Morinigo-Sotelo, Daniel Romero-Troncoso, Rene Pons-Llinares, Joan fault detection fault diagnosis frequency analysis induction motors rotating machines signal processing spectral analysis time-frequency decompositions bearing diagnosis early damage detection unlabeled learning deep learning dynamic information fusion induction motor electric machine machine learning supervised learning data-driven power connection failures condition monitoring induction machines negative sequence currents shorted turn faults phasor compensation Prony method broken rotor bar fast Fourier transform current signal analysis artificial intelligence early detection fault severity incipient fault fault-tolerant control AC machines back EMF feedforward compensation multiple coupled circuit model parameter identification fault classification measurement techniques physical variables signal analysis ITSC fault traction motor apFFT SVM thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology In modern industries, induction motors are the backbone of numerous applications, powering everything from manufacturing facilities to transportation systems. While they are known for their reliability, unexpected failures can still occur, leading to increased operational costs, facility damage, or service interruptions. "Early Detection and Fault Diagnosis of Induction Motors" is a comprehensive volume that compiles ten innovative journal articles focused on maintaining these machines. The papers explore a variety of techniques that introduce new ideas to the field. 2023-11-30T20:58:05Z 2023-11-30T20:58:05Z 2023 book ONIX_20231130_9783036593357_299 9783036593357 9783036593340 https://directory.doabooks.org/handle/20.500.12854/128847 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/8317 https://mdpi.com/books/pdfview/book/8317 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-9334-0 10.3390/books978-3-0365-9334-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036593357 9783036593340 200 Basel open access |
| spellingShingle | fault detection fault diagnosis frequency analysis induction motors rotating machines signal processing spectral analysis time-frequency decompositions bearing diagnosis early damage detection unlabeled learning deep learning dynamic information fusion induction motor electric machine machine learning supervised learning data-driven power connection failures condition monitoring induction machines negative sequence currents shorted turn faults phasor compensation Prony method broken rotor bar fast Fourier transform current signal analysis artificial intelligence early detection fault severity incipient fault fault-tolerant control AC machines back EMF feedforward compensation multiple coupled circuit model parameter identification fault classification measurement techniques physical variables signal analysis ITSC fault traction motor apFFT SVM thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Early Detection of Faults in Induction Motors |
| title | Early Detection of Faults in Induction Motors |
| title_full | Early Detection of Faults in Induction Motors |
| title_fullStr | Early Detection of Faults in Induction Motors |
| title_full_unstemmed | Early Detection of Faults in Induction Motors |
| title_short | Early Detection of Faults in Induction Motors |
| title_sort | early detection of faults in induction motors |
| topic | fault detection fault diagnosis frequency analysis induction motors rotating machines signal processing spectral analysis time-frequency decompositions bearing diagnosis early damage detection unlabeled learning deep learning dynamic information fusion induction motor electric machine machine learning supervised learning data-driven power connection failures condition monitoring induction machines negative sequence currents shorted turn faults phasor compensation Prony method broken rotor bar fast Fourier transform current signal analysis artificial intelligence early detection fault severity incipient fault fault-tolerant control AC machines back EMF feedforward compensation multiple coupled circuit model parameter identification fault classification measurement techniques physical variables signal analysis ITSC fault traction motor apFFT SVM thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | fault detection fault diagnosis frequency analysis induction motors rotating machines signal processing spectral analysis time-frequency decompositions bearing diagnosis early damage detection unlabeled learning deep learning dynamic information fusion induction motor electric machine machine learning supervised learning data-driven power connection failures condition monitoring induction machines negative sequence currents shorted turn faults phasor compensation Prony method broken rotor bar fast Fourier transform current signal analysis artificial intelligence early detection fault severity incipient fault fault-tolerant control AC machines back EMF feedforward compensation multiple coupled circuit model parameter identification fault classification measurement techniques physical variables signal analysis ITSC fault traction motor apFFT SVM thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20231130_9783036593357_299 |