Fig. 1: The framework of knowledge-driven unsupervised learning strategy.
a The proposed unsupervised learning strategy framework is illustrated as follows: (1) The Acoustic Emission (AE) spectrum derived from experimental input is divided into three regions based on their statistical features: Region 1 (R1) is dislocation dominant, Region 3 (R3) is crack dominant, and Region 2 (R2) is a mix of both mechanisms. (2) Corresponding acoustic waveforms of the AE spectrum. (3) Power spectrum densities (PSD) are extracted from AE waveforms. (4) The Knowledge-Infused Aggregate Loss Function (KIALF) dispatches base learners (GDC) in an unsupervised fashion. (5) The best overall performing base learner is elected as the ultimate choice for the backbone model. (6) New AE waveforms from subsequent experiments are input into the system. (7) The signals are separated and further analyzed using the proposed approach. (8) Early failure warning is conducted based on the separated signals. b Essential steps of the proposed approach. A set of unlabeled AE signals Z is applied to a base learner GDCn, yielding a probabilistic classification output COn. The ratios of crack-related signals in two randomly sampled intervals ri = [si, ei] and rj = [sj, ej] are calculated as \({{{\mathcal{T}}}}_{n}({r}_{i})\) and \({{{\mathcal{T}}}}_{n}({r}_{j})\), respectively. The aggregate trend metric \({{{\mathcal{L}}}}_{{\mbox{Trend}},n,i,j}\) is computed for the interval pair. The full-period loss KIALF (\({{{\mathcal{L}}}}_{n}\)) is obtained via repeated sampling and backpropagated to optimize the base learner parameters θn. The optimized learner GDCnopt is produced by minimizing \({{{\mathcal{L}}}}_{n}\).
