Accurate identification of damage in CFRP structures under limited sample conditions is critical for ensuring the safety of in-service structures. To detect damage of CFRP structures, a cascaded wavelet scattering transform and 1D convolutional neural network (WST-1DCNN) method was proposed in this paper. First, to capture the primary damage features in Lamb wave signals, a wavelet scattering transform stage was designed. On this basis, a 1D convolutional neural network stage was designed to identify damage in CFRP structures. This stage could autonomously extract multi-scale hierarchical features through its layered architecture. Finally, the proposed method was verified on a finite element simulation data and an authoritative public CFRP composites data set of NASA prognostics data repository. The results demonstrated that the proposed WST-1DCNN method achieved better performance on test samples containing damage types absent in the training set. This paper provides a feasible method for identifying damage in CFRP structures under limited sample conditions.
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