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September 30, 2025Proceedings

The improved CNNBILSTM hybrid model for predicting compressor stall

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Authors

RLRuiyang LianHDHefang DengXQXiaoqing Qiang

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Overview

This analysis reveals that a hybrid CNN_BiLSTM model improves compressor stall predictions, suggesting enhanced reliability in aircraft safety.

Key Points

  • The CBLSTM model improved prediction accuracy for compressor stall events significantly.
  • Using the NASA Rotor67 dataset, the model demonstrated effective performance under various operating conditions.
  • The hybrid approach leverages Conv1D for feature extraction and BiLSTM for managing temporal relationships.
  • These findings underscore the potential of advanced deep learning techniques in enhancing aviation safety measures.

Cite This Study

Lian et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e3f8a7d58c25ebb215bhttps://doi.org/10.33737/gpps25-tc-213
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