Damage sensitive PCA-FRF feature in unsupervised machine learning for damage detection of plate-like structures

Siow, Pei Yi and Ong, Zhi Chao and Khoo, Shin Yee and Lim, Kok-Sing (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). ISSN 0219-4554, DOI https://doi.org/10.1142/S0219455421500280.

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Damage detection is important in maintaining the integrity and safety of structures. The vibration-based Structural Health Monitoring (SHM) methods have been explored and applied extensively by researchers due to its non-destructive manner. The damage sensitivity of features used can significantly affect the accuracy of the vibration-based damage identification methods. The Frequency Response Function (FRF) was used as a damage sensitive feature in several works due to its rich yet compact representation of dynamic properties of a structure. However, utilizing the full size of FRFs in damage assessment requires high processing and computational time. A novel reduction technique using Principal Component Analysis (PCA) and peak detection on raw FRFs is proposed to extract the main damage sensitive feature while maintaining the dynamic characteristics. A rectangular Perspex plate with ground supports, simulating an automobile, was used for damage assessment. The damage sensitivity of the extracted feature, i.e. PCA-FRF is then evaluated using unsupervised k-means clustering results. The proposed method is found to exaggerate the shift of damaged data from undamaged data and improve the repeatability of the PCA-FRF. The PCA-FRF feature is shown to have higher damage sensitivity compared to the raw FRFs, in which it yielded well-clustered results even for low damage conditions.

Item Type: Article
Uncontrolled Keywords: Damage sensitive; Frequency response function; Principal component analysis; Structural health monitoring; Unsupervised clustering
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 03 Mar 2022 03:19
Last Modified: 03 Mar 2022 03:19
URI: http://eprints.um.edu.my/id/eprint/26443

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