Random vibration load identification of a cylindrical structure using data Deep Recurrent Neural Network.
Saved in:
| Title: | Random vibration load identification of a cylindrical structure using data Deep Recurrent Neural Network. |
|---|---|
| Authors: | Ouyang, Qinshan1 (AUTHOR), Dong, Longlei1 (AUTHOR) dongll@xjtu.edu.cn, Zhou, Jiaming2 (AUTHOR), Liu, Jian1 (AUTHOR) |
| Source: | International Journal of Applied Electromagnetics & Mechanics. Jul2025, Vol. 78 Issue 1-3, p35-39. 5p. |
| Subjects: | Random vibration, Recurrent neural networks, Machine learning, Strains & stresses (Mechanics), Generalization, Cylinder (Shapes), Long short-term memory |
| Abstract: | A novel data-driven Deep Recurrent Neural Network (data-DRNN) method is proposed for the time-domain load identification of a cylindrical structure subjected to random vibration excitation by an electromagnetic shaker. The data-DRNN model comprises of two Long Short-Term Memory (LSTM) layers and one Bidirectional LSTM (BLSTM) layer, and is trained using a large dataset of loads and corresponding responses data. The effectiveness and accuracy of the proposed method have been validated by analyzing the trapezoidal spectrum excited response data under various temperature conditions. Furthermore, the model's generalization capability has been evaluated by examining different testing target spectrums. The results indicate that data-DRNN has excellent accuracy and generalization ability, making it a promising choice for load identification of random vibration. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Applied Electromagnetics & Mechanics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | A novel data-driven Deep Recurrent Neural Network (data-DRNN) method is proposed for the time-domain load identification of a cylindrical structure subjected to random vibration excitation by an electromagnetic shaker. The data-DRNN model comprises of two Long Short-Term Memory (LSTM) layers and one Bidirectional LSTM (BLSTM) layer, and is trained using a large dataset of loads and corresponding responses data. The effectiveness and accuracy of the proposed method have been validated by analyzing the trapezoidal spectrum excited response data under various temperature conditions. Furthermore, the model's generalization capability has been evaluated by examining different testing target spectrums. The results indicate that data-DRNN has excellent accuracy and generalization ability, making it a promising choice for load identification of random vibration. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 13835416 |
| DOI: | 10.1177/13835416251329172 |
