  Conventional damage localization algorithms used in ultrasonic guided
wave-based structural health monitoring (GW-SHM) rely on physics-defined
features of GW signals. In addition to requiring domain knowledge of the
interaction of various GW modes with various types of damages, they also suffer
from errors due to variations in environmental and operating conditions (EOCs)
in practical use cases. While several machine learning tools have been reported
for EOC compensation, they need to be custom-designed for each combination of
damage and structure due to their dependence on physics-defined feature
extraction. In this work, we propose a CNN-based automated feature extraction
framework coupled with Gaussian mixture model (GMM) based EOC compensation and
damage classification and localization method. Features learnt by the CNNs are
used for damage classification and localization of damage by modeling the
probability distribution of the features using GMMs. The Kullback-Leibler (KL)
divergence of these GMMs with respect to corresponding baseline GMMs are used
as signal difference coefficients (SDCs) to compute damage indices (DIs) along
various GW sensor paths, and thus for damage localization. The efficacy of the
proposed method is demonstrated using FE generated GW-data for an aluminum
plate with a network of six lead zirconate titanate (PZT) sensors, for three
different types of damages (rivet hole, added mass, notch) at various
temperatures, with added white noise and pink noise to incorporate errors due
to EOCs. We also present experimental validation of the method through
characterization of notch damage in an aluminum panel under varying and
non-uniform temperature profiles, using a portable custom-designed field
programmable gate array (FPGA) based signal transduction and data acquisition
system.
