Chinese Journal of Ship Research, Volume. 19, Issue 6, 117(2024)

Neural network-based evaluation method for alignment state of air spring vibration isolation device

Zhiwei LIU1...2, Liang SHI1,2, and Song LIU12 |Show fewer author(s)
Author Affiliations
  • 1Institute of Noise and Vibration, Naval University of Engineering, Wuhan 430033, China
  • 2National Key Laboratory on Ship Vibration and Noise, Wuhan 430033, China
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    Objectives

    To address the difficulty in accurately describing the states of alignment under nonlinear and time-varying conditions in existing monitoring models, a neural network-based method for evaluating the states of alignment is proposed.

    Method

    A BP neural network-based prediction model is developed. The typical working conditions for acquiring training and testing data are defined, and the data is denoised using a moving average filter. The rules for adjusting the model's hyperparameters are summarized. Experimental studies are then carried out on both small and large air spring isolation devices.

    Results

    The results demonstrate that the neural network model can accurately predict the states of isolation devices alignment using only the air spring pressure data. The model exhibits strong generalizability across different device types, with a prediction error of less than 0.5 and an alignment prediction accuracy of 96.29%.

    Conclusion

    The proposed model does not rely on system parameters and performs well in predicting the states of alignment for both small and large devices. The results of this study can provide theoretical support for the state prediction of alignment in a dynamic way and shaft alignment control of power equipment after startup.

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    Zhiwei LIU, Liang SHI, Song LIU. Neural network-based evaluation method for alignment state of air spring vibration isolation device[J]. Chinese Journal of Ship Research, 2024, 19(6): 117

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    Paper Information

    Category: Theory and Method of Intelligent Design for Ship and Ocean Engineering

    Received: May. 23, 2024

    Accepted: --

    Published Online: Mar. 14, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03933

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