Aiming to address the problem of extracting the remote sensing FTIR spectral characteristics of the hot jet of a certain type of aero-engine under different working conditions, this paper proposes a feature construction algorithm for the remote sensing FTIR spectral characteristics of the aero-engine hot jet based on the fusion of the original spectral features and the deep spectral features. The infrared spectrum was collected at a distance of 280 m, covering the spectral range of 2.5–15 μm with a resolution of 1 cm−1. The Neighborhood–Autoencoder Integration Dual-Branch Network (NAIDN) feature construction algorithm is proposed. This algorithm contains a neighborhood integration branch and an autoencoder branch. The neighborhood integration branch converts the radiation intensity values of discrete wavenumber points into local energy aggregation features through a sliding window, accurately extracting the key physical information in the original spectrum. The autoencoder branch uses a three-layer fully connected neural network architecture to mine the deep spectral features of the spectral data. The algorithms of the two branches not only retain the physical interpretability of spectral analysis but also capture the multi-parameter coupling information hidden in the hot jet spectrum through the representation learning ability of the autoencoder, achieving feature fusion across spatial dimensions. Compared with traditional feature construction algorithms, the dual-branch feature construction algorithm proposed in this paper has stronger comprehensive representation capabilities. The content of carbon dioxide (CO2) and cyanide groups (-C≡N) in the hot jet under different operating conditions varies significantly. In the experiment, an unsupervised clustering algorithm, the Agglomerative Clustering classifier, is selected, and the classification accuracy of the features extracted by the algorithm in this paper reaches 92.97% on this classifier, thereby verifying the effectiveness of the algorithm in this paper.
Kang et al. (Thu,) studied this question.