A Semi-Supervised Federated Learning Fault Diagnosis Method Based on Adaptive Class Prototype Points for Data Suffered by High Missing Rate-Reference-Cited by-同舟云学术

A Semi-Supervised Federated Learning Fault Diagnosis Method Based on Adaptive Class Prototype Points for Data Suffered by High Missing Rate

Published:2023-12 Issue:4 Volume:109 Page:
ISSN:0921-0296
Container-title:Journal of Intelligent & Robotic Systems
language:en
Short-container-title:J Intell Robot Syst

Author:

Zhou Funa^ORCID,Xu Wei,Wang Chaoge,Hu Xiong,Wang Tianzhen

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Industrial and Manufacturing Engineering,Mechanical Engineering,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10846-023-02025-8.pdf

Reference51 articles.

1. Sánchez, P.J.B., Asensio, M.T., Papaelias, M., Márquez, F.P.G.: Life Cycle Assessment in Autonomous Marine Vehicles. In: Xu, J., García Márquez, F.P., Ali Hassan, M.H., Duca, G., Hajiyev, A., Altiparmak, F. (eds) Proceedings of the Fifteenth International Conference on Management Science and Engineering Management (ICMSEM). Lecture Notes on Data Engineering and Communications Technologies, vol 79. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79206-0_17

2. Sánchez, P.J.B., Márquez, F.P.G., Papaelias, M., Marini, S., Govindaraj, S., Durand, L.: Innovative Technologies Developed for Autonomous Marine Vehicles by ENDURUNS Project. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (edsUbiquitous Intelligent Systems (ICUIS). Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-2541-2_22

3. Wang, H., Li, Y.F.: Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments. Reliab. Eng. Syst. Saf. 233, 109102 (2023). https://doi.org/10.1016/j.ress.2023.109102

4. Min, H.G., Fang, Y.K., Wu, X., Lei, X.P., Chen S.X., Teixeira, R., Zhu, B., Zhao, X.M., Xu Z.G.: A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis. Expert Syst. Appl. 224, 120002 (2023). https://doi.org/10.1016/j.eswa.2023.120002

5. Özgür Gültekin, Eyup Cinar, Kemal Özkan, Ahmet Yazıcı: Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle. Expert Syst. Appl. 200, 117055 (2022). https://doi.org/10.1016/j.eswa.2022.117055