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...
| Autores: | , , , , , , , , |
|---|---|
| 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 |
| Sumario: | 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. |
|---|