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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
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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.
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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
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KEYWORDS
Data modeling
Machine learning
Diagnostics
Feature extraction
Clouds
Vibration
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