Hybrid Modal-Machine Learning Damage Diagnosis
Integrates modal-based and machine learning (ML) based methods to develop a more damage-sensitive, noise robust, and generalised damage diagnosis method
Motivation of This Research
Cold-Start Problem faced by ML methods
Damage events are rare, which causes the cold-start problem for machine learning methods that usually requires several labelled examples of healthy and damaged conditions for training.
The need for a noise robust modal analysis method for damage diagnosis
Conventional modal analysis requires shutdown conditions, which is not practical for modal-based damage diagnosis methods.
Damage sensitive features required for unsupervised methods
Modal parameters, i.e., natural frequencies are commonly used as the damage-sensitive feature for unsupervised methods. However, as modal parameters are not the upstream data (FRFs), they could incur extraction-errors that affect their sensitivity towards damage.
Main Features of the Proposed Approach
Damage-sensitive, noise robust, and solves the cold-start problem
ISMA
Makes the program to be noise robust
PCA-FRF
Used as a damage-sensitive feature for 1st level unsupervised damage presence detection
MSA
Used as a single damage classifier to solve the cold-start issue and auto labels the damage samples
Hybrid ML
Combines unsupervised and supervised methods to diagnose unseen single and multiple damages
Tested on Plate-like and Beam-like Structures
Generalised enough to locate unseen single and multiple damages for both plate and beam-like structures
Research Outputs
- Siow, P. Y., Ong, Z. C., Khoo, S. Y., & Lim, K. S. (2021). Damage Sensitive PCA-FRF Feature in Unsupervised Machine Learning for Damage Detection of Plate-Like Structures. International Journal of Structural Stability and Dynamics, 21(2), 29. https://doi.org/10.1142/s0219455421500280
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Siow, P. Y., Ong, Z. C., Khoo, S. Y., & Lim, K. S. (in press, 2023). Noise robustness of an operational modal-based structural damage-detection scheme using Impact-Synchronous Modal Analysis. Journal of Zhejiang University-Science A. https://doi.org/10.1631/jzus.A2200620
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Siow, P. Y., Ong, Z. C., Khoo, S. Y., Lim, K. S., & Chew, B. T. (2023). Hybrid machine learning with mode shape assessment for damage identification of plates. Smart Structures and Systems. (accepted)
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Gordan, M., Siow, P. Y., Deifalla, A. F., Chao, O. Z., Ismail, Z., & Yee, K. S. (2022). Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures. Ieee Access, 10, 84837-84852. https://doi.org/10.1109/access.2022.3197776