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Domain adversarial neural network-based nonlinear system identification for helicopter transmission system

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Abstract

Helicopter transmission system identification provides model support for fault diagnosis, health monitoring, controller design and so on. The great advantages of deep neural networks are shown in strong nonlinear identification such as helicopter transmission systems. In addition, transfer learning has advantages in the identification of helicopter transmission system with complex and changeable working conditions. In deep transfer learning, domain adversarial neural network can establish a generalization model in complex and variable working conditions, and fast and high-precision identification can be achieved by the network-based transfer method. Therefore, in order to take into account the accuracy and speediness of strong nonlinear helicopter transmission system identification with complicated and variable working conditions, this paper proposes a nonlinear system identification strategy based on domain adversarial neural network in deep transfer learning, which combines pre-trained and fine-tuning. Firstly, the domain adversarial neural network is pre-trained offline under various working conditions. Then, the pre-trained model is fine-tuned online in a single working condition to quickly obtain the high-precision model. The proposed nonlinear system identification strategy can achieve fast and accurate online identification under different working conditions. Finally, the experiment is carried out on the helicopter transmission test platform. The experimental results show that the pre-trained model with four working conditions can achieve high-precision identification under 16 working conditions. Furthermore, the model is fine-tuned based on the pre-trained model. The fine-tuning time is reduced by 41.5% compared to the training time of the same structure model.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 52241502), and the National defense technology basic research project of China (No. JSZL2022110A).

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Authors and Affiliations

  1. National Key Lab of Aerospace Power System and Plasma Technology, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Tian Chen, Xingwu Zhang, Chenxi Wang, Xiaolei Yu & Xuefeng Chen

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Tian Chen, Xingwu Zhang, Chenxi Wang, Xiaolei Yu & Xuefeng Chen

  3. Department of Avionics and Weapon Engineering, Army Aviation Institute, Beijing, 101123, China

    Shicheng Wang

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  1. Tian Chen
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  2. Xingwu Zhang
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Correspondence to Xingwu Zhang.

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Chen, T., Zhang, X., Wang, C. et al. Domain adversarial neural network-based nonlinear system identification for helicopter transmission system. Nonlinear Dyn 111, 14695–14711 (2023). https://doi.org/10.1007/s11071-023-08657-7

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