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

Document Type

Article

Publication Date

2-1-2021

Abstract

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.

Keywords

Damage sensitive, Frequency response function, Principal component analysis, Structural health monitoring, Unsupervised clustering

Publication Title

International Journal of Structural Stability and Dynamics

Divisions

fac_eng

Volume

21

Issue

2

Publisher

World Scientific Publishing

Publisher Location

5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE

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