Neural Networks based Speed-Torque Estimators for Induction Motors and Performance Metrics
This paper focuses on the quantitative analysis of deep neural networks used in data-driven modeling of induction motor dynamics. With the availability of a large amount of data generated by industrial sensor networks, it is now possible to train deep neural networks. Recently researchers have start...
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| Veröffentlicht in: | IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society S. 495 - 500 |
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18.10.2020
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| ISSN: | 2577-1647 |
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| Abstract | This paper focuses on the quantitative analysis of deep neural networks used in data-driven modeling of induction motor dynamics. With the availability of a large amount of data generated by industrial sensor networks, it is now possible to train deep neural networks. Recently researchers have started exploring the usage of such networks for physics modeling, online control, monitoring, and fault prediction in induction motor operations. We consider the problem of estimating speed and torque from currents and voltages of an induction motor. Neural networks provide quite good performance for this task when analysed from a machine learning perspective using standard metrics. We show, however, that there are some caveats in using machine learning metrics to analyze a neural network model when applied to induction motor problems. Given the mission- critical nature of induction motor operations, the performance of neural networks has to be validated from an electrical engineering point of view. To this end, we evaluate several traditional neural network architectures and recent state of the art architectures on dynamic and quasi-static benchmarks using electrical engineering metrics. |
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| AbstractList | This paper focuses on the quantitative analysis of deep neural networks used in data-driven modeling of induction motor dynamics. With the availability of a large amount of data generated by industrial sensor networks, it is now possible to train deep neural networks. Recently researchers have started exploring the usage of such networks for physics modeling, online control, monitoring, and fault prediction in induction motor operations. We consider the problem of estimating speed and torque from currents and voltages of an induction motor. Neural networks provide quite good performance for this task when analysed from a machine learning perspective using standard metrics. We show, however, that there are some caveats in using machine learning metrics to analyze a neural network model when applied to induction motor problems. Given the mission- critical nature of induction motor operations, the performance of neural networks has to be validated from an electrical engineering point of view. To this end, we evaluate several traditional neural network architectures and recent state of the art architectures on dynamic and quasi-static benchmarks using electrical engineering metrics. |
| Author | Jebai, Al Kassem Verma, Sagar Henwood, Nicolas Castella, Marc Pesquet, Jean-Christophe |
| Author_xml | – sequence: 1 givenname: Sagar surname: Verma fullname: Verma, Sagar organization: Université Paris-Saclay, CentraleSupelec, Inria,Centre de Vision Numérique – sequence: 2 givenname: Nicolas surname: Henwood fullname: Henwood, Nicolas organization: Schneider Toshiba Inverter Europe – sequence: 3 givenname: Marc surname: Castella fullname: Castella, Marc organization: Institut Polytechnique de Paris,SAMOVAR, Télécom SudParis – sequence: 4 givenname: Al Kassem surname: Jebai fullname: Jebai, Al Kassem organization: Schneider Toshiba Inverter Europe – sequence: 5 givenname: Jean-Christophe surname: Pesquet fullname: Pesquet, Jean-Christophe organization: Université Paris-Saclay, CentraleSupelec, Inria,Centre de Vision Numérique |
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| Snippet | This paper focuses on the quantitative analysis of deep neural networks used in data-driven modeling of induction motor dynamics. With the availability of a... |
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| StartPage | 495 |
| SubjectTerms | deep learning induction motor Induction motors Mathematical model Measurement Neural networks time series Torque Training Trajectory |
| Title | Neural Networks based Speed-Torque Estimators for Induction Motors and Performance Metrics |
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