| Title: |
Neural network-enabled condition monitoring of DC-Link capacitors in three-phase inverters |
| Authors: |
Fassi, Youssof; Zhao, Shuai; Wei, Xing; Heiries, Vincent; Boutet, Jérôme; Boisseau, Sebastien; Wang, Huai |
| Contributors: |
Département Systèmes (DSYS); Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI); Direction de Recherche Technologique (CEA) (DRT (CEA)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)); Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA); Department of Energy Aalborg (AAU ENERGY); Aalborg University (AAU); European Project: 101131278,HORIZON-MSCA-2022-SE-01,HORIZON-MSCA-2022-SE-01,TEAMING(2024) |
| Source: |
PCIM 2025 - International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management ; https://cea.hal.science/cea-05199245 ; PCIM 2025 - International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, May 2025, Nuremberg, Germany. ⟨10.30420/566541025⟩ ; https://www.vde-verlag.de/proceedings-de/566541025.html |
| Publisher Information: |
CCSD |
| Publication Year: |
2025 |
| Collection: |
HAL-CEA (Commissariat à l'énergie atomique et aux énergies alternatives) |
| Subject Terms: |
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [SPI.NRJ]Engineering Sciences [physics]/Electric power |
| Subject Geographic: |
Nuremberg; Germany |
| Description: |
International audience ; This work addresses condition monitoring of DC-link capacitors in three-phase inverters to ensure motor drive reliability. A neural network model is trained based on the DC-Link capacitor voltage data during a discharging operation mode, minimizing extra hardware needs. Experimental results on diverse operating conditions achieved 98% of accuracy, with 100% positive predictive value and 95% sensitivity, demonstrating robust classification and diagnostics across varying unseen operating conditions. |
| Document Type: |
conference object |
| Language: |
English |
| Relation: |
info:eu-repo/grantAgreement//101131278/EU/e-powerTrain prEdictive mAintenance using physics inforMed learnING/TEAMING |
| DOI: |
10.30420/566541025 |
| Availability: |
https://cea.hal.science/cea-05199245; https://cea.hal.science/cea-05199245v1/document; https://cea.hal.science/cea-05199245v1/file/PCIM2025_final_paper_yf.pdf; https://doi.org/10.30420/566541025 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.55920249 |
| Database: |
BASE |