Lamb wave based monitoring of fatigue crack growth using principal component analysis

Fatigue crack growth in metallic plates was monitored using Lamb waves which were generated and captured by surface-mounted piezoelectric wafers in a pitch-catch configuration. Instead of directly pinpointing signal segments to quantify wave scattering caused by the existence of crack damage and related severity, principal component analysis (PCA), as an efficient approach for information compression and classification, was undertaken to distinguish different structural conditions due to fatigue crack growth. For this purpose, a variety of statistical parameters in the time domain as damage indices were extracted from the wave signals. A series of contaminated counterparts with different signal-to-noise ratios were also simulated to increase the statistical size of the data set. It was concluded that PCA is capable of reducing the dimensions of a complex set of original data, whose information can be represented and highlighted by the first few principal components. With the assistance of PCA, the different structural conditions attributable to crack growth can be classified.