Vessel Recognition in Induction Heating Appliances - A Deep-Learning Approach

Villa, J. ; Navarro, D. (Universidad de Zaragoza) ; Dominguez, A. ; Artigas, J.I. (Universidad de Zaragoza) ; Barragan, L.A. (Universidad de Zaragoza)

Resumen: The selection of a vessel by an induction-hob user has a significant impact on the performance of the appliance. Due to the induction heating physical phenomena, there exist many factors that modify the equivalent impedance of induction hobs and, consequently, the operational conditions of the inverter. In particular, the type of vessel, which is a sole decision of the user, strongly affects these parameters. Besides, the ferromagnetic properties of the different materials the vessels are made with, vary differently with the excitation level, and given that most of the domestic induction hobs are based on an ac-bus voltage arrangement, the excitation level continuously varies. The algorithm proposed in this work takes advantage of this fact to identify the equivalent impedance of the load and recognize the pot. This is accomplished through a phase-sensitive detector that was already proposed in the literature and the application of deep learning. Different convolutional neural networks are tested on an augmented experimental-based dataset and the proposed algorithm is implemented in an experimental prototype with a system-on-chip. The proposed implementation is presented as an effective and accurate method to characterize and discriminate between different pots that could enable further functionalities in new generations of induction hobs.

Idioma: Inglés

DOI: 10.1109/ACCESS.2021.3052864

Año: 2021

Publicado en: IEEE Access 9 (2021), 16053-16061
ISSN: 2169-3536

Factor impacto JCR: 3.476 (2021)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 79 / 164 = 0.482 (2021) - Q2 - T2
Categ. JCR: TELECOMMUNICATIONS rank: 43 / 93 = 0.462 (2021) - Q2 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 105 / 277 = 0.379 (2021) - Q2 - T2

Factor impacto CITESCORE: 6.7 - Engineering (Q1) - Computer Science (Q1) - Materials Science (Q1)

Factor impacto SCIMAGO: 0.927 - Computer Science (miscellaneous) (Q1) - Engineering (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/FSE
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2019-103939RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PTQ-17-09045
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/RTC-2017-5965-6

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)