Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation

Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Mo...

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Autores principales:	Carla Terron-Santiago, Javier Martinez-Roman, Ruben Puche-Panadero, Angel Sapena-Bano
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fault diagnosis sparse identification model order reduction induction machines Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/0f98acd325dd4ad8b9caf67cb9f0bc8b
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spelling	oai:doaj.org-article:0f98acd325dd4ad8b9caf67cb9f0bc8b2021-11-11T19:00:46ZLow-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation10.3390/s212169631424-8220https://doaj.org/article/0f98acd325dd4ad8b9caf67cb9f0bc8b2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6963https://doaj.org/toc/1424-8220Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Moreover, they should run in real time to develop online condition-monitoring (CM) systems. Hybrid finite element method (FEM)-analytical models have been recently proposed for fault diagnosis purposes since they keep good accuracy, which is widely accepted, and they can run in real-time simulators. However, these models still require the full simulation of the FEM model to compute the parameters of the analytical model for each faulty scenario with its corresponding computing needs. To address these drawbacks (large computing power and memory resources requirements) this paper proposes sparse identification techniques in combination with the trigonometric interpolation polynomial for the computation of IM model parameters. The proposed model keeps accuracy similar to a FEM model at a much lower computational effort, which could contribute to the development and to the testing of condition-monitoring systems. This approach has been applied to develop an IM model under static eccentricity conditions, but this may extend to other fault types.Carla Terron-SantiagoJavier Martinez-RomanRuben Puche-PanaderoAngel Sapena-BanoMDPI AGarticlefault diagnosissparse identificationmodel order reductioninduction machinesChemical technologyTP1-1185ENSensors, Vol 21, Iss 6963, p 6963 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fault diagnosis sparse identification model order reduction induction machines Chemical technology TP1-1185
spellingShingle	fault diagnosis sparse identification model order reduction induction machines Chemical technology TP1-1185 Carla Terron-Santiago Javier Martinez-Roman Ruben Puche-Panadero Angel Sapena-Bano Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
description	Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Moreover, they should run in real time to develop online condition-monitoring (CM) systems. Hybrid finite element method (FEM)-analytical models have been recently proposed for fault diagnosis purposes since they keep good accuracy, which is widely accepted, and they can run in real-time simulators. However, these models still require the full simulation of the FEM model to compute the parameters of the analytical model for each faulty scenario with its corresponding computing needs. To address these drawbacks (large computing power and memory resources requirements) this paper proposes sparse identification techniques in combination with the trigonometric interpolation polynomial for the computation of IM model parameters. The proposed model keeps accuracy similar to a FEM model at a much lower computational effort, which could contribute to the development and to the testing of condition-monitoring systems. This approach has been applied to develop an IM model under static eccentricity conditions, but this may extend to other fault types.
format	article
author	Carla Terron-Santiago Javier Martinez-Roman Ruben Puche-Panadero Angel Sapena-Bano
author_facet	Carla Terron-Santiago Javier Martinez-Roman Ruben Puche-Panadero Angel Sapena-Bano
author_sort	Carla Terron-Santiago
title	Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
title_short	Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
title_full	Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
title_fullStr	Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
title_full_unstemmed	Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation
title_sort	low-computational-cost hybrid fem-analytical induction machine model for the diagnosis of rotor eccentricity, based on sparse identification techniques and trigonometric interpolation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0f98acd325dd4ad8b9caf67cb9f0bc8b
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Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation

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