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Analysis of device state parameter correlations and multimodal data model construction based on neural networks

Author

Listed:
  • Xiaoyang Li
  • Xiang Dong
  • Xiaokun Han
  • Bi Zhao
  • Shuwei Yi
  • Jian Bai
  • Xuanwei Zhang

Abstract

Traditional methods for monitoring equipment status struggle to meet the analytical demands of high-dimensional data inherent in complex systems. To address this, this paper proposes a neural network-based approach for analyzing the correlations among equipment status parameters, combined with a multimodal data model. A denoising autoencoder is employed to construct a deep neural network (DNN) for fault feature extraction, while a hierarchical DNN (HDNN) algorithm is introduced to optimize feature extraction in multimodal environments. The accuracy of fault classification reached 98.05%. Comparative analysis with various models demonstrates the superiority of HDNN in fault classification and severity recognition.

Suggested Citation

  • Xiaoyang Li & Xiang Dong & Xiaokun Han & Bi Zhao & Shuwei Yi & Jian Bai & Xuanwei Zhang, 2025. "Analysis of device state parameter correlations and multimodal data model construction based on neural networks," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 488-494.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:488-494.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf015
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