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Aiming at the typical failure problems of normally open and normally closed attitude control nozzles of air and space vehicles, the CNN algorithm is used to extract high-dimensional features from the time series data, and the LSTM model is used to learn the temporal features of the time series data, so as to establish a CNN-LSTM nozzle failure prediction model, and carry out the prediction analysis. The results show that compared with the two benchmark models of CNN and LSTM, the CNN-LSTM prediction model has smaller mean absolute error rate and mean square error rate, and the prediction effect is better.

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Intelligent Fault Diagnosis Technique for Attitude Control Nozzle of Air and Space Vehicle Based on CNN-LSTM

  • 会议论文

  • 发表日期：

    2026年

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  • Pengcheng Dong,
  • Kunfeng Lu,
  • Na Yao,
  • Yuxin Pan,
  • Yujia Xie

摘要

Aiming at the typical failure problems of normally open and normally closed attitude control nozzles of air and space vehicles, the CNN algorithm is used to extract high-dimensional features from the time series data, and the LSTM model is used to learn the temporal features of the time series data, so as to establish a CNN-LSTM nozzle failure prediction model, and carry out the prediction analysis. The results show that compared with the two benchmark models of CNN and LSTM, the CNN-LSTM prediction model has smaller mean absolute error rate and mean square error rate, and the prediction effect is better.