## 1 Introduction
In this study, the focus is on classifying PTCs curves under different connection conditions, making this assumption challenging. Only a small fraction of PTCs may experience thread damage or improper connections, making the acquisition of a substantial number of corresponding PTCs curves exceedingly difficult. Hence, it is crucial to develop an intelligent diagnostic method that can effectively classify PTC curves under a small sample size. A viable solution to handle the issue of an insufficient training dataset is data augmentation. Generally, data augmentations can artificially inflate the training dataset by either data warping or oversampling [21; 22]. Data warping refers to introducing real-world distortions, such as elastic deformation, random cropping, rotation, and scaling, into the training data. In contrast, oversampling augmentations generate entirely new samples to supplement the original training dataset. The generative adversarial network (GAN), as one of the most innovative oversampling augmentations, has gained considerable attention since its introduction in 2014 [23]. Unlike other data augmentation techniques, GAN learns the distribution patterns of samples in the training dataset and generates synthetic specimens with the original features. Numerous studies have shown that GAN has been receiving increasing attention in the field of small-sample fault diagnosis, achieving promising results [24; 25; 26; 27; 28; 29]. [PERSON] et al. [26] presented a multi-module gradient penalized GANto address small sample problems in the intelligent fault diagnosis schemes of machines. The GAN's generator can create mechanical signals under different conditions and further expand the training dataset, improving the classification performance. [PERSON] et al. [30] proposed an enhanced generative adversarial network (E-GAN), which employs a deep convolutional generative adversarial network (DCGAN) to balance the data and a modified CNN diagnosis model for fault classification. [PERSON] et al. [31] addressed the issue of imbalanced distribution of fault samples in rotating machinery by proposing an auxiliary classifier Wasserstein generative adversarial network (ACWGAN), which is capable of generating high-quality samples for minority classes. Inspired by the success of deep learning in various domains of classification tasks and its potential in data augmentation, this paper proposes an improved AoT-DCGAN and T-CNN hybrid deep learning model for the intelligent recognition of PTCs curves under small samples. The proposed method consists of two stages: data generation and curve classification. The first stage involves building an AoT-DCGAN model to learn the feature distribution from original PTCs curves and generate synthetic samples to inflate the training dataset. The second stage proposes a T-CNN model for classifying PTCs curves, trained using an augmented dataset. The results are compared with datasets at different augmentation ratios and traditional machine learning models to validate the superiority of the proposed method in terms of classification performance. The main contributions of this paper are as follows:  
1. We design a hybrid deep learning model for the automatic classification of PTCs curves. The model achieves good classification results under small samples, enabling the fast and quantitative evaluation of the connection quality of PTCs. 2. We propose an AoT-DCGAN model capable of generating samples that closely resemble real PTCs curves, exhibiting high authenticity and accuracy. Furthermore, the model training process is more stable, effectively addressing the issue of gradient vanishing. 3. We introduce a T-CNN model for classifying PTCs curves which achieves superior classification performance compared to traditional machine learning and deep learning models. The organization of this paper is as follows. In Section 2, the basic concepts of the dataset used in this study are described.