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A PINN-CNN Hybrid Architecture for Vibration-Based Damage Detection in Airship Envelopes

Author

Listed:
  • Fangyuan Zhao

    (China University of Geosciences (Wuhan), School of Mechanical Engineering and Electronic Information)

  • Yiwei Cheng

    (China University of Geosciences (Wuhan), School of Mechanical Engineering and Electronic Information
    Shenzhen Research Institute of China University of Geosciences)

  • Keqiang Xie

    (Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology)

  • Yuanhang Wang

    (Shenzhen Technology University, Sino-German College of Intelligent Manufacturing)

Abstract

Conventional methods for detecting damage in flexible airship envelopes face limitations in accurately evaluating local material properties under conditions of large deformation. A Physics-Informed Neural Networks Convolutional Neural Networks (PINN-CNN) hybrid architecture is proposed, integrating physical constraints derived from wave equations and membrane dynamics into the neural network training process. The approach was evaluated using synthetic airship envelope data under three conditions: intact, 2 × 2 cross-cut damage, and 5 × 5 cross-cut damage. Experimental findings indicate that the PINN-CNN model attained 100% accuracy, representing a notable improvement over the traditional CNN, which achieved an accuracy of 96.67%, while also exhibiting superior robustness to noise. Under conditions with 25% noise, the PINN-CNN model maintained an accuracy of 90.00%, whereas the standard CNN performance declined to 70.00%.

Suggested Citation

  • Fangyuan Zhao & Yiwei Cheng & Keqiang Xie & Yuanhang Wang, 2026. "A PINN-CNN Hybrid Architecture for Vibration-Based Damage Detection in Airship Envelopes," Springer Series in Reliability Engineering,, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-032-22873-4_26
    DOI: 10.1007/978-3-032-22873-4_26
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