Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Smart Structures and Systems Volume 31, Number 5, May 2023 , pages 485-500 DOI: https://doi.org/10.12989/sss.2023.31.5.485 |
|
|
|
Hybrid machine learning with mode shape assessment for damage identification of plates |
||
Pei Yi Siow, Zhi Chao Ong, Shin Yee Khoo, Kok-Sing Lim and Bee Teng Chew
|
||
| Abstract | ||
| 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 precollected 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. | ||
| Key Words | ||
| frequency response function; machine learning; mode shape; principal component analysis; structural damage identification | ||
| Address | ||
| (1) Pei Yi Siow, Zhi Chao Ong, Shin Yee Khoo, Bee Teng Chew: Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; (2) Zhi Chao Ong, Shin Yee Khoo: Centre of Research Industry 4.0 (CRI 4.0), Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia; (3) Kok-Sing Lim: Photonics Research Centre, Deputy Vice Chancellor (Research & Innovation) Office, Universiti Malaya, 50603 Kuala Lumpur, Malaysia. | ||