Pulsed terahertz spectroscopy combined with hybrid machine learning approaches for structural health monitoring of multilayer thermal barrier coatings.

Abstract

Structural health monitoring of multilayer thermal barrier coatings (TBCs) is very vital to ensure the structural integrity and service performance of the hot-section components of the aero-engine. In this paper, we theoretically and numerically demonstrated that the terahertz time domain spectrum and the terahertz reflectance spectrum could be adopted to estimate the structure parameters, based on the finite difference time domain (FDTD) algorithm, 64 samples which were imported with three kinds of 64 sets structure parameters had been calculated to obtain the time domain and terahertz reflectance signals. To mimic the actual test signals, the original FDTD simulation signals were processed by adding the Gaussian white noise and wavelet noise reduction. To reduce the data dimension and improve the calculation efficiency during modeling, the principal component analysis (PCA) algorithm was adopted to reduce the dimensions of time-domain data and reflectance data. Finally, these data after multiple signal processing and PCA feature extraction were used to train the extreme learning machine (ELM), combining the genetic algorithm (GA) could optimize the PCA-ELM model and further improve the prediction performance of the hybrid model. Our proposed novel and efficient terahertz nondestructive technology combined with the hybrid machine learning approaches provides great potential applications on the multilayer TBCs structural integrity evaluation.