Machine learning based classification of guided wave signals in the context of inter-specimen variabilities

Vivek Nerlikar (1) , Olivier Mesnil (1) , Roberto Miorelli (1) , Oscar D’almeida (2)

1 LSMA (CEA, LIST) - Laboratoire de Simulation, Modélisation et Analyse (CEA, LIST)
2 Safran Tech

In the context of Guided Wave-based Structural Health Monitoring (GW-SHM), ultrasonic elastic waves are used to detect damages in structures by comparing the acquired signals with those from a defectfree structure. However, the high sensitivity of GWs to environmental and operational conditions limits the validity of such references. Notably, variabilities between multiple specimens are often significant from the GWs perspective. These variabilities are particularly important in composites and are due to sensor positioning, sensor coupling and material variability. This communication presents a baseline-free approach using physics-enhanced Machine Learning (ML) for enhanced robustness. To ensure the coverage of these variabilities the approach is validated on multiple Carbon-fiber-reinforced polymers (CFRP) panels. The methodology relies on feature extraction from raw GW signals and training classification algorithms (e.g., kernel machines, neural networks). To make the classifier learn inter-specimen variabilities, an experimental database of 45 impacted composite panels is used. Half of them are used to provide pristine data, and the rest to provide damaged data so that the same sample is either in the training or test set, but never in both. Good classification performance is obtained, demonstrating that the classifier has successfully learnt to recognize defect signatures despite the variability linked to the multiple specimens and instrumentations.

Mots clés: Guided Waves based SHM; Baseline-free approach; Machine learning; Material and instrumentation variabilities; CFRP; Artificial intelligence; Structural Health Monitoring; ultrasound; instrumentation; ultrasonic elastic waves; damage detection; defect; sensor; sensor positioning; sensor coupling; material variability; multiple Carbon-fiber-reinforced polymers; signal processing; classification; online learning; kernel machine; neural network; defect signature

Domaines: Traitement du signal et de l'image [eess.SP]

Vivek Nerlikar, Olivier Mesnil, Roberto Miorelli, Oscar D’almeida. Machine learning based classification of guided wave signals in the context of inter-specimen variabilities. Lecture Notes in Civil Engineering, 2023, 270, pp.452-461. ⟨10.1007/978-3-031-07322-9_46⟩. ⟨cea-04555886⟩