CNN-GRU based method for peak location of reflected Terahertz signals from thermal barrier coatings

Due to the limitation of spraying conditions, the microstructure of Thermal Barrier Coatings (TBCs) is not homogeneous, so it is important to extract the peak information of the reflected signal efficiently and accurately using Terahertz (THz) non-destructive detection technique for online thickness measurement. To this end, a hybrid model peak information extraction method based on a convolutional neural network (CNN) and a gated recurrent unit (GRU) is proposed. Firstly, a theoretical model of the THz signal is used to generate the simulated signal; secondly, a 1D-CNN is used to extract features adaptively from the temporal signal input, and a GRU is constructed to learn the temporal information between feature vectors to complete the extraction of peak information; finally, a calibration strategy is employed to determine the highest value around the original localisation result in order to eliminate localisation error and achieve peak location. The proposed CNN-GRU model predicts evaluation metrics that are 2–4 times smaller than those of other methods, and the running time is reduced to 45.71% compared to networks with close prediction accuracy. The method can reduce manual involvement and time expenses while maintaining prediction accuracy and that it provides a new intelligent way for online thickness measuring.