@inproceedings{69b70c4d02c14c86a63e379c4b3e99c4,
title = "Improved Mobile-Vit model and its application in rolling bearing fault diagnosis",
abstract = "In order to improve the efficiency and accuracy of rolling bearing fault diagnosis, FDMT fault diagnosis model was proposed. Firstly, based on the Mobile-Vit model, a FDMT model for fault diagnosis with one-dimensional rolling bearing vibration signal as direct input was designed, and the structure of the FDMT model was given. Secondly, the FDMT model was trained and tested using the CWRU data set to verify the effectiveness of the model. Then, the influence of hyperparameter adjustment on the performance of FDMT model was discussed. Finally, the FDMT model was compared with other deep learning models. The results show that the improved Mobile-Vit model has higher fault diagnosis accuracy.",
keywords = "FDMT, Mobile-ViT, fault diagnosis, rolling bearings",
author = "Huina Mu and Xiaoyun Zeng and Wei Liu and Jialin Sun and Yeshu Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 6th International Conference on System Reliability and Safety Engineering, SRSE 2024 ; Conference date: 11-10-2024 Through 14-10-2024",
year = "2024",
doi = "10.1109/SRSE63568.2024.10772545",
language = "English",
series = "2024 6th International Conference on System Reliability and Safety Engineering, SRSE 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "308--315",
booktitle = "2024 6th International Conference on System Reliability and Safety Engineering, SRSE 2024",
address = "United States",
}