FTIR-SpectralGAN: A Spectral Data Augmentation Generative Adversarial Network for Aero-Engine Hot Jet FTIR Spectral Classification

Abstract

Aiming at the overfitting problem caused by the limited sample size in the spectral classification of aero-engine hot jets, this paper proposed a synthetic spectral enhancement classification network FTIR-SpectralGAN for the FT-IR of aeroengine hot jets. Firstly, passive telemetry FTIR spectrometers were used to measure the hot jet spectrum data of six types of aero-engines, and a spectral classification dataset was created. Then, a spectral classification network FTIR-SpectralGAN was designed, which consists of a generator and a discriminator. The generator architecture comprises six Conv1DTranspose layers, with five of these layers integrated with BN and LeakyReLU layers to introduce noise injection. This design enhances the generation capability for complex patterns and facilitates the transformation from noise to high-dimensional data. The discriminator employs a multi-task dual-output structure, consisting of three Conv1D layers combined with LeakyReLU and Dropout techniques. This configuration progressively reduces feature dimensions and mitigates overfitting. During training, the generator learns the underlying distribution of spectral data, while the discriminator distinguishes between real and synthetic data and performs spectral classification. The dataset was randomly partitioned into training, validation, and test sets in an 8:1:1 ratio. For training strategy, an unbalanced alternating training approach was adopted, where the generator is trained first, followed by the discriminator and then the generator again. Additionally, weighted mixed loss and label smoothing strategies were introduced to enhance network training performance. Experimental results demonstrate that the spectral classification accuracy reaches up to 99%, effectively addressing the overfitting issue commonly encountered in CNN-based classification tasks with limited samples. Comparative experiments show that FTIR-SpectralGAN outperforms classical data augmentation methods and CVAE-based synthetic data enhancement approaches. It also achieves higher robustness and classification accuracy compared to other spectral classification methods.