Exploring Supervised and Unsupervised Learning with 1D Autoencoders: Three Case Studies

In this study, the application of one-dimensional convolutional autoencoders is investigated under supervised, one-class unsupervised, and full unsupervised learning paradigms and across three case studies, respectively. The results demonstrate the adaptability of convolutional autoencoders to diverse sensing modalities and problem settings, with each paradigm offering complementary strengths depending on data availability. Promising outcomes across all three scenarios suggest that the proposed frameworks can address heterogeneous challenges in non-destructive testing and beyond.