CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13081 > Article
Paper
1 April 2024 Edge-cloud collaborative transfer learning based on stacked sparse autoencoder with data regularization
Fajia Li, Shihu Zhao, Huanyong Cui, Haining Liu, Jing Xu
Author Affiliations +
Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130810Y (2024) https://doi.org/10.1117/12.3025719
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China
Abstract
Edge-cloud collaboration provides a better solution for condition monitoring, which can reduce response time while maintaining computational efficiency. In practical condition monitoring scenarios, the individual differences among equipment often decrease the accuracy of diagnostic models. To tackle this problem, a transfer learning method based on stacked sparse autoencoder is proposed, which employs a data regularization strategy to improve feature extraction ability. The fault diagnosis model trained in the cloud transfers its model parameters and structure to the edge side. By a finetuning process with a small amount of data, and the model is further updated for condition monitoring of the individual machine. The experimental results show that the proposed KT-SAE method has improved transfer accuracy compared to other related transfer learning methods.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Fajia Li, Shihu Zhao, Huanyong Cui, Haining Liu, and Jing Xu "Edge-cloud collaborative transfer learning based on stacked sparse autoencoder with data regularization", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130810Y (1 April 2024); https://doi.org/10.1117/12.3025719
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
;
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Machine learning
Diagnostics
Feature extraction
Clouds
Vibration
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top