Smart Structures and Systems

  • 제31권5호
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  • Pages.485-500
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  • 2023
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Hybrid machine learning with mode shape assessment for damage identification of plates

  • Pei Yi Siow (Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya) ;
  • Zhi Chao Ong (Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya) ;
  • Shin Yee Khoo (Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya) ;
  • Kok-Sing Lim (Photonics Research Centre, Deputy Vice Chancellor (Research & Innovation) Office, Universiti Malaya) ;
  • Bee Teng Chew (Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya)
  • 투고 : 2022.06.06
  • 심사 : 2023.03.17
  • 발행 : 2023.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

키워드

과제정보

The authors wish to acknowledge the financial support and advice given by Impact-Oriented Interdisciplinary Research Grant (IIRG007B-2019), private funding by SD Advance Engineering Sdn Bhd (PV032-2018), Advanced Shock and Vibration Research (ASVR) Group of University of Malaya and other project collaborators.

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