Abstract

Accurate fatigue crack width estimation is crucial for aircraft safety, however, limited research exists on (i) the direct relationship between fatigue crack width and Lamb wave signatures and (ii) probabilistic artificial intelligence (AI) approach for automated analysis using acoustic emission waveforms. This paper presents a probabilistic deep learning approach for fatigue crack width estimation, employing an automated wavelet feature extractor and probabilistic Bayesian neural network. A dataset constituting the fatigue experiment on aluminum lap-joint specimens is considered, in which Lamb wave signals were recorded at several time instants for each specimen. Signals acquired from the piezo actuator-receiver sensor pairs are related to the optically measured surface crack length. The sensitive features are automatically extracted from the signals using decomposition techniques called maximal overlap discrete wavelet transform (MODWT). The extracted features are then mapped through the deep learning model, which incorporates Bayesian inference to account for both aleatoric as well as epistemic uncertainty, that provides outcomes in the form of providing probabilistic estimates of crack width with uncertainty quantification. Thus, employing an automated wavelet feature extractor (MODWT) on a dataset of fatigue experiments, the framework learns the relationship between Lamb wave signals and crack width. Validation on unseen in-situ data demonstrates the efficacy of the approach for practical implementation, paving the way for more reliable fatigue life prognosis.

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