Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things

In multi-chip IGBT power modules, package-level failures typically manifest first as localized distortions in the copper baseplate temperature field before evolving into catastrophic faults. However, existing Power Internet of Things (Power IoT) monitoring approaches mainly rely on single-point indi...

Descripción completa

Detalles Bibliográficos
Autores: Du, Xingfeng, Yang, Yuan, Lv, Jiahui, Xiang, Wei, Zhang, Dao Hua, Geng, Qi, Kang, Rui, Wen, Yang, Cóbreces Álvarez, Santiago|||0000-0002-3308-4613
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:dnet:ebuahbibliot::53aa7aa65235d5e77ad2f9008a8e7230
Acceso en línea:http://hdl.handle.net/10017/69351
https://dx.doi.org/10.1109/JIOT.2026.3694843
Access Level:acceso abierto
Palabra clave:Power Internet of Things
Edge computing
IGBT power module
Temperature field reconstruction
Physics-constrained neural network
Sparse sensing
Electrónica
Electronics
id ES_dad688154ee4fb45fa976bc99be8482f
oai_identifier_str oai:dnet:ebuahbibliot::53aa7aa65235d5e77ad2f9008a8e7230
network_acronym_str ES
network_name_str España
repository_id_str
spelling Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of ThingsDu, XingfengYang, YuanLv, JiahuiXiang, WeiZhang, Dao HuaGeng, QiKang, RuiWen, YangCóbreces Álvarez, Santiago|||0000-0002-3308-4613Power Internet of ThingsEdge computingIGBT power moduleTemperature field reconstructionPhysics-constrained neural networkSparse sensingElectrónicaElectronicsIn multi-chip IGBT power modules, package-level failures typically manifest first as localized distortions in the copper baseplate temperature field before evolving into catastrophic faults. However, existing Power Internet of Things (Power IoT) monitoring approaches mainly rely on single-point indicators, such as on-state voltage drop or case temperature, making them inadequate for capturing spatially nonuniform thermal anomalies under sparse sensing. This paper proposes a terminal–edge–cloud collaborative method for parallel multi-module temperature-field reconstruction in Power IoT, which maps sparse thermocouple measurements to a high-resolution temperature field while meeting real-time constraints. At the terminal layer, the thermocouple array layout is optimized using a condition-number minimization criterion to maximize system observability. At the edge layer, a physics-constrained conditional generative adversarial network (PC-cGAN) is deployed on an MPSoC platform, and a multi-objective particle swarm optimization algorithm is used to automatically search network architectures and hyperparameters under resource constraints, thereby balancing reconstruction accuracy and inference latency. Experimental results demonstrate that a single edge node can monitor 4–6 power modules in parallel, with an inference latency of 11.8 ms for four-module parallel processing, achieving a 3.1× efficiency improvement over serial architectures. Using 12 sparse measurements, the proposed method achieves a root mean square error of 1.89°C, a mean absolute error of 1.52°C, and a hotspot deviation of 2.34°C; compared with CNN baselines, these metrics improve by 29.5%, 29.3%, and 32.2%, respectively, while reducing communication bandwidth by 98% relative to cloud-centric approaches. The proposed method provides an engineering-ready solution for scalable, real-time thermal-state monitoring of power-module arrays in converter systems.Agencia Estatal de InvestigaciónNational Natural Science Foundation of ChinaIEEE20262026-05-20journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/69351https://dx.doi.org/10.1109/JIOT.2026.3694843reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Not available PID2024-158935OB-C22Not available Not available 62174134Not available Not available 62574163open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:ebuahbibliot::53aa7aa65235d5e77ad2f9008a8e72302026-06-18T11:13:07Z
dc.title.none.fl_str_mv Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
title Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
spellingShingle Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
Du, Xingfeng
Power Internet of Things
Edge computing
IGBT power module
Temperature field reconstruction
Physics-constrained neural network
Sparse sensing
Electrónica
Electronics
title_short Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
title_full Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
title_fullStr Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
title_full_unstemmed Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
title_sort Terminal-Edge-Cloud Collaborative Temperature Field Reconstruction for Multi-chip IGBT Power Modules in Power Internet of Things
dc.creator.none.fl_str_mv Du, Xingfeng
Yang, Yuan
Lv, Jiahui
Xiang, Wei
Zhang, Dao Hua
Geng, Qi
Kang, Rui
Wen, Yang
Cóbreces Álvarez, Santiago|||0000-0002-3308-4613
author Du, Xingfeng
author_facet Du, Xingfeng
Yang, Yuan
Lv, Jiahui
Xiang, Wei
Zhang, Dao Hua
Geng, Qi
Kang, Rui
Wen, Yang
Cóbreces Álvarez, Santiago|||0000-0002-3308-4613
author_role author
author2 Yang, Yuan
Lv, Jiahui
Xiang, Wei
Zhang, Dao Hua
Geng, Qi
Kang, Rui
Wen, Yang
Cóbreces Álvarez, Santiago|||0000-0002-3308-4613
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Power Internet of Things
Edge computing
IGBT power module
Temperature field reconstruction
Physics-constrained neural network
Sparse sensing
Electrónica
Electronics
topic Power Internet of Things
Edge computing
IGBT power module
Temperature field reconstruction
Physics-constrained neural network
Sparse sensing
Electrónica
Electronics
description In multi-chip IGBT power modules, package-level failures typically manifest first as localized distortions in the copper baseplate temperature field before evolving into catastrophic faults. However, existing Power Internet of Things (Power IoT) monitoring approaches mainly rely on single-point indicators, such as on-state voltage drop or case temperature, making them inadequate for capturing spatially nonuniform thermal anomalies under sparse sensing. This paper proposes a terminal–edge–cloud collaborative method for parallel multi-module temperature-field reconstruction in Power IoT, which maps sparse thermocouple measurements to a high-resolution temperature field while meeting real-time constraints. At the terminal layer, the thermocouple array layout is optimized using a condition-number minimization criterion to maximize system observability. At the edge layer, a physics-constrained conditional generative adversarial network (PC-cGAN) is deployed on an MPSoC platform, and a multi-objective particle swarm optimization algorithm is used to automatically search network architectures and hyperparameters under resource constraints, thereby balancing reconstruction accuracy and inference latency. Experimental results demonstrate that a single edge node can monitor 4–6 power modules in parallel, with an inference latency of 11.8 ms for four-module parallel processing, achieving a 3.1× efficiency improvement over serial architectures. Using 12 sparse measurements, the proposed method achieves a root mean square error of 1.89°C, a mean absolute error of 1.52°C, and a hotspot deviation of 2.34°C; compared with CNN baselines, these metrics improve by 29.5%, 29.3%, and 32.2%, respectively, while reducing communication bandwidth by 98% relative to cloud-centric approaches. The proposed method provides an engineering-ready solution for scalable, real-time thermal-state monitoring of power-module arrays in converter systems.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/69351
https://dx.doi.org/10.1109/JIOT.2026.3694843
url http://hdl.handle.net/10017/69351
https://dx.doi.org/10.1109/JIOT.2026.3694843
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Not available PID2024-158935OB-C22
Not available Not available 62174134
Not available Not available 62574163
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421617774329856
score 15,811543

Similares en LA Referencia