An Improved AoT-DCGAN and T-CNN Hybrid Deep Learning Model for Intelligent Diagnosis of PTCs Quality under Small Sample Space

Abstract

The intelligent diagnosis of premium threaded connections (PTCs) is vital for ensuring the robust and leak-proof performance of tubing under high-temperature, high-pressure, acidic gas conditions. However, achieving accurate diagnostic results necessitates a substantial number of PTCs curves under diverse make-up conditions, presenting considerable challenges in practical industrial detection. In this study, we introduce an end-to-end classification model, which combines an asynchronously optimized two-dimensional deep convolutional generative adversarial network (AoT-DCGAN) and a two-dimensional convolutional neural network (T-CNN), designed to enhance the classification performance under small sample size. Our proposed method first leverages AoT-DCGAN to identify the distribution patterns of the original samples and generate synthetic counterparts. Concurrently, we implement a novel weight optimization strategy, termed asynchronous optimization (AO), to alleviate the issue of gradient vanishing during the generator’s optimization phase. Following this, a novel T-CNN model is devised and trained on the enlarged dataset to automate the classification of PTCs curves. The performance evaluation of our method, based on recall, specificity, F1-score, precision values, and confusion matrices at varying data augmentation ratios, demonstrates that the model’s classification capabilities are enhanced as the dataset size escalates, peaking at a dataset size of 1200. Moreover, given the same training set, the T-CNN model outperforms traditional machine learning and deep learning models, achieving classification accuracies of up to 95.9%, 95.5%, and 96.7% for the AC, ATI, and NDT curves, respectively. Lastly, it was confirmed that applying asynchronous optimization in the DCGAN training process results in a more consistent decline in the loss function.