Principal Component Analysis (PCA) plays a significant role in SHM field. There
are plenty of algorithms that use PCA either directly or indirectly to detect damages
in structures. Although PCA shows a successful role in damage detection but it still
needs a complimentary step for automatic damage classification. It means a human
effort still is required to classify different clusters that exists. Among different clas-
sifiers, the wavelet classifier posses many dedicated merits. This work concentrates
on automatic classification of damages with different severities. To do this, PCA is
used as a tool for dimensionality reduction and then a wavelet classifier is applied
on the result to classify different patterns in the structure each of which associated
to significant state of the structure. This work involves experiments with composite
plates powered by piezoelectric transducers as sensors and actuators. Damages are
introduced into the structure as mass with different weights.
CitacióGaribnezhad, F. [et al.]. Automatic damage classification based on wave cluster and principal component analysis. A: International Workshop on Structural Health Monitoring. "Structural Health Monitoring 2013: A Roadmap to Intelligent Structures". Stanford: 2013, p. 2760-2767.