CRACK GROWTH MONITORING WITH HIERARCHICAL CLUSTERING OF AE

This paper presents a sequence of signal processing and hierarchical clustering techniques utilized to process signals with low signal-to-noise ratio (SNR) measured by multiple AE sensors. Noise and other extraneous events present major challenges for the detection and monitoring of AE signals generated by the inception of microcracks and their growth. Characteristics of AE waveforms released during controlled mode-I fracture are explored, and these characteristics are used for clustering AE and locating the fracture. With hierarchical clustering and signal “denoising” techniques, it is possible to locate the position of the crack plane with high accuracy using AE signals of poor quality, wherein the spatial distribution of clusters is indicative of crack propagation.