$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Three-Phase Inverter Fault Diagnosis Based on an Improved Deep Residual Network 원문보기

Electronics, v.12 no.16, 2023년, pp.3460 -   

Fu, Yanfang (School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China) ,  Ji, Yu (School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China) ,  Meng, Gong (Beijing Aerospace Automatic Control Institution, Beijing 100854, China) ,  Chen, Wei (Beijing Aerospace Automatic Control Institution, Beijing 100854, China) ,  Bai, Xiaojun (School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, China)

Abstract AI-Helper 아이콘AI-Helper

This study addresses the challenges of limited fault samples, noise interference, and low accuracy in existing fault diagnosis methods for three-phase inverters under real acquisition conditions. To increase the number of samples, Wavelet Packet Decomposition (WPD) denoising and a Conditional Variat...

참고문헌 (24)

  1. Cao Fault Diagnosis Technique for Three-level Inverters Based on ICEEMDAN-FE and Support Vector Machine Locomot. Electr. Drive 2023 1 97 

  2. 10.3390/electronics9081251 Chen, T. (2021). Research on Open-Circuit Fault Diagnosis Method for Three-Phase Voltage Source Inverters. [Ph.D. Thesis, University of Science and Technology Beijing]. 

  3. Song A Review of Fault Diagnosis Methods for Multilevel Inverters Micromotors 2019 52 110 

  4. Xu Diagnosing Open-Circuit Faults in Three-Level Grid-Connected Inverters Based on an Adaptive Sliding-Mode Observer Trans. China Electrotech. Soc. 2023 38 1010 

  5. Fan Early Fault Parameter Identification for Grid-Connected Inverters Based on a Model J. China Three Gorges Univ. (Nat. Sci.) 2022 44 64 

  6. Li A Fast Diagnosis Strategy for Inverter Open-Circuit Faults Based on the Current Path of Brushless DC Motors IEEE Trans. Power Electron. 2023 10.1109/TPEL.2023.3270030 38 9311 

  7. Shen Diagnosis of Open-Circuit Faults in Three-Phase Inverters Based on CNN and Analysis of Sample Conditions J. Natl. Univ. Defense Technol. 2022 44 163 

  8. Yu Inverter Fault Diagnosis Method Based on Convolutional Neural Network Automot. Eng. 2022 44 142 

  9. Xu Fault Diagnosis of Open-Circuit Switches in Three-Phase Inverters Based on Output Voltage Trajectory Proc. CSEE 2023 43 1 

  10. Zhu Extraction of IGBT Minor Fault Features Based on Multimodal Output Voltage of Inverter Electr. Mach. Control 2023 27 65 

  11. Sun An Open-Circuit Fault Diagnosis Algorithm Based on Signal Normalization Preprocessing for Motor Drive Inverter IEEE Trans. Instrum. Meas. 2023 72 3513712 

  12. Yang Inverter Fault Diagnosis Based on Fourier Transform and Evolutionary Neural Network Front. Energy Res. 2023 10.3389/fenrg.2022.1090209 10 1090209 

  13. Yan Open-Circuit Fault Diagnosis in Voltage Source Inverter for Motor Drive by Using Deep Neural Network Eng. Appl. Artif. Intell. 2023 10.1016/j.engappai.2023.105866 120 105866 

  14. Sun Inverter Fault Diagnosis Method Based on CGAN-CNN under Imbalanced Samples J. Chin. Soc. Power Eng. 2022 42 1 

  15. Cui T-Type Inverter Fault Diagnosis Based on GASF and Improved AlexNet Energy Rep. 2023 10.1016/j.egyr.2023.01.095 9 2718 

  16. 10.3390/act12030125 Łuczak, D., Brock, S., and Siembab, K. (2023). Fault Detection and Localization of a Three-Phase Inverter with Permanent Magnet Synchronous Motor Load Using a Convolutional Neural Network. Actuators, 12. 

  17. Li A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects IEEE Trans. Neural Netw. Learn. Syst. 2021 10.1109/TNNLS.2021.3084827 33 6999 

  18. 10.1109/CVPR.2016.90 He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA. 

  19. Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv. 

  20. Gao, R.X., and Yan, R. (2011). Wavelets: Theory and Applications for Manufacturing, Springer. 

  21. 10.1109/CVPR.2018.00745 Hu, J., Shen, L., and Sun, G. (2018, January 18). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. 

  22. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models Proc. ICML 2013 30 1 

  23. Xue Dung Beetle Optimizer: A New Meta-heuristic Algorithm for Global Optimization J. Supercomput. 2023 10.1007/s11227-022-04959-6 79 7305 

  24. Xue A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm Syst. Sci. Control. Eng. 2020 10.1080/21642583.2019.1708830 8 22 

관련 콘텐츠

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로