Fault diagnosis method for embedded electromechanical systems based on 1DCNN-BiGRU

Tanbao Yan; Jiayu Wu; Zehua Liu; Yixuan Zhao

doi:10.1117/12.3031178

19 July 2024 Fault diagnosis method for embedded electromechanical systems based on 1DCNN-BiGRU

Tanbao Yan, Jiayu Wu, Zehua Liu, Yixuan Zhao

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318142 (2024) https://doi.org/10.1117/12.3031178
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Due to the nonlinear and uncertain characteristics of aerospace electromechanical systems, the temporal information is challenging to fully exploit during the fault diagnosis process. In order to address this issue and further enhance the accuracy of fault diagnosis models, this paper proposes a fault diagnosis approach that integrates a One-Dimensional Convolutional Neural Network (1D-CNN) with Bidirectional Gated Recurrent Unit (Bi-GRU). Initially, a sliding time window method is employed to prepare samples from the raw data as input for the 1D-CNN. Subsequently, the features extracted from the convolutional and pooling layers are fed into the Bi-GRU, and the diagnostic results are output through a fully connected layer. Finally, the algorithm model is embedded and deployed in the Xilinx Zynq 7Z100 environment. The proposed algorithm is validated, and the algorithm model undergoes optimization using the High Level Synthesis (HLS) tool to demonstrate the feasibility and necessity of model optimization.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tanbao Yan, Jiayu Wu, Zehua Liu, and Yixuan Zhao "Fault diagnosis method for embedded electromechanical systems based on 1DCNN-BiGRU", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318142 (19 July 2024); https://doi.org/10.1117/12.3031178

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