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High Precision Feature Fast Extraction Strategy for Aircraft Attitude Sensor Fault Based on RepVGG and SENet

Zhen Jia1, Kai Wang2, Yang Li2

  • 1School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces a novel strategy for diagnosing aircraft attitude sensor faults, enhancing both speed and accuracy. The advanced method ensures safer flights by improving fault detection capabilities.

Keywords:
RepVGGattention mechanismattitude sensorfault diagnosistime-frequency signal

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Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Aircraft attitude sensors are critical for flight control and safety.
  • Existing fault diagnosis methods often compromise between detection speed and accuracy.
  • Reliable fault diagnosis is essential to prevent flight accidents.

Purpose of the Study:

  • To propose a fast and high-precision fault diagnosis strategy for aircraft attitude sensors.
  • To address the limitations of current methods in balancing diagnosis rate and accuracy.
  • To enhance overall aircraft flight safety through improved sensor fault detection.

Main Methods:

  • Developed aircraft dynamics and attitude sensor fault models.
  • Utilized the SENet attention mechanism for weighting time-domain and time-frequency fault signals.
  • Employed a RepVGG-based convolutional neural network for deep feature extraction and classification.

Main Results:

  • The proposed strategy demonstrated a significant improvement in fault diagnosis speed.
  • High precision in identifying sensor faults was achieved.
  • The method offers a favorable trade-off between diagnosis precision and speed.

Conclusions:

  • The developed fault diagnosis strategy effectively enhances both the speed and accuracy of aircraft attitude sensor fault detection.
  • This approach contributes to improved flight safety by providing reliable and timely fault information.
  • The integration of attention mechanisms and deep learning offers a promising direction for aircraft sensor fault diagnosis.