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A Multi-Modal Decision-Level Fusion Framework for Hypervelocity Impact Damage Classification in Spacecraft.

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  • 1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

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Summary
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Spacecraft damage from hypervelocity impacts (HVIs) is hard to assess. A new multimodal fusion framework accurately identifies damage, improving spacecraft structural health monitoring.

Keywords:
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Area of Science:

  • Aerospace Engineering
  • Materials Science
  • Signal Processing

Background:

  • Spacecraft face hypervelocity impacts (HVIs) from micrometeoroids and debris during on-orbit service.
  • Diverse damage types from HVIs pose challenges for accurate structural health assessment.
  • Unimodal approaches struggle with damage classification due to noise and varying imaging distances.

Purpose of the Study:

  • To develop a physics-informed multimodal fusion framework for enhanced spacecraft damage assessment.
  • To overcome limitations of unimodal approaches in identifying diverse HVI damage patterns.
  • To improve the accuracy and reliability of on-orbit structural health monitoring.

Main Methods:

  • Integrated a distance-aware infrared enhancement strategy with vibration spectral subtraction for data alignment.
  • Employed a dual-stream ResNet coupled with Dempster-Shafer (D-S) evidence theory for inter-modal conflict resolution.
  • Developed a physics-informed approach to fuse heterogeneous data qualities.

Main Results:

  • Achieved a mean accuracy of 99.01%, significantly outperforming unimodal baselines (92.96% and 97.11%).
  • Demonstrated high precision (>96.9%) across all damage categories, including micro-cracks and perforations.
  • Showcased high stability with a low standard deviation of 0.74% in damage assessment.

Conclusions:

  • The proposed multimodal fusion framework effectively enhances on-orbit damage assessment accuracy.
  • This approach provides a robust solution for spacecraft structural health monitoring, overcoming unimodal limitations.
  • Validated the efficacy of integrating infrared and vibration data with advanced fusion techniques for precise damage detection.