Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals

نویسندگان

چکیده

This paper presents a new structural health monitoring strategy based on deep learning architecture that uses nonlinear ultrasonic signals for the automatic assessment of breathing-like debonds in lightweight stiffened composite panels (SCPs). Towards this, finite element simulations guided wave (GW) response SCPs and laboratory-based experiments have been undertaken multiple with without baseplate-stiffener using fixed network piezoelectric transducers (actuators/sensors). GW time domain are collected from sensors onboard these frequency represent signatures as existence higher harmonics. These harmonic separated GWs (raw) converted to images time–frequency scalograms continuous wavelet transforms. A is designed convolutional neural automatically extract discrete image features characterization SCP under healthy variable breathing-debond conditions. The proposed learning-aided demonstrates promising autonomous inspection potential high accuracy such complex structures subjected multi-level regions.

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ژورنال

عنوان ژورنال: Composite Structures

سال: 2023

ISSN: ['0263-8223', '1879-1085']

DOI: https://doi.org/10.1016/j.compstruct.2023.116876