Fatigue Damage Monitoring of Composite Structures Based on Lamb Wave Propagation and Multi-Feature Fusion

Feiting Zhang, Kaifu Zhang, Hui Cheng, Dongyue Gao, Keyi Cai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

To address the challenges associated with fatigue damage monitoring in load-bearing composite structures, we developed a method that utilizes Lamb wave propagation and partial least squares regression (PLSR) for effective monitoring. Initially, we extracted diverse characteristics from both the time and frequency domains of the Lamb wave signal to capture the essence of the damage. Subsequently, we constructed a PLSR model, leveraging Lamb wave multi-feature fusion, specifically tailored for in-service fatigue damage monitoring. The efficacy of our proposed approach in quantitatively monitoring fatigue damage was thoroughly validated through rigorous standard fatigue tests. In practical applications, our model effectively mitigated the impact of multicollinearity among feature variables on model accuracy. Furthermore, the PLSR model demonstrated superior accuracy compared to the PCR model, given an equal number of principal components. To strike a harmonious balance between efficiency and precision, we optimized the size of the feature variable. The results show that the optimized PLSR model achieved an R-squared value exceeding 97% in predicting the in-service damage area. This underscores the robustness and reliability of our method in accurately monitoring fatigue damage in load-bearing composite structures.

Original languageEnglish
Article number423
JournalJournal of Composites Science
Volume8
Issue number10
DOIs
StatePublished - Oct 2024

Keywords

  • fatigue damage monitoring
  • Lamb wave
  • multi-feature fusion
  • multicollinearity
  • partial least squares regression

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