Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data
School of Astronautics, Beihang University, Beijing 100191, China
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Sensors 2020, 20(19), 5615; https://doi.org/10.3390/s20195615
Received: 1 September 2020
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Revised: 26 September 2020
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Accepted: 28 September 2020
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Published: 1 October 2020
(This article belongs to the Section Intelligent Sensors)
Finite element model updating precision depends heavily on sufficient vibration feature extraction. However, adequate amount of sample collection is generally time-consuming in frequency response (FR) model updating. Accurate vibration feature extraction with insufficient data has become a significant challenge in FR model updating. To update the finite element model with a small dataset, a novel approach based on transfer learning is firstly proposed in this paper. A readily available fault diagnosis dataset is selected as ancillary knowledge to train a high-precision mapping from FR data to updating parameters. The proposed transfer learning network is constructed with two branches: source and target domain feature extractor. Considering about the cross-domain feature discrepancy, a domain adaptation method is designed by embedding the extracted features into a shared feature space to train a reliable model updating framework. The proposed method is verified by a simulated satellite example. The comparison results manifest that sample amount dependency has prominently lessened this method and the updated model outperforms the method without transfer learning in accuracy with the small dataset. Furthermore, the updated model is validated through dynamic response out of the training set.
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Keywords:
model updating; frequency response; deep convolutional neural network; transfer learning; domain adaptation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Deng, Z.; Zhang, X.; Zhao, Y. Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data. Sensors 2020, 20, 5615. https://doi.org/10.3390/s20195615
AMA Style
Deng Z, Zhang X, Zhao Y. Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data. Sensors. 2020; 20(19):5615. https://doi.org/10.3390/s20195615
Chicago/Turabian StyleDeng, Zhongmin, Xinjie Zhang, and Yanlin Zhao. 2020. "Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data" Sensors 20, no. 19: 5615. https://doi.org/10.3390/s20195615
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