Image-Based Condition Recognition for Refined Oil Pipelines Using WGANAugmented Enhanced CNN

Accurate identification of operating conditions in refined oil pipelines is vital for maintaining transportation safety and preventing anomalies amid escalating energy demands. However, frequent field operations induce variations in pipeline conditions, and existing methods struggle with non-uniform sample dimensions across these conditions, complicating precise recognition due to overlooking latent heterogeneities and often suffering from suboptimal performance caused by imbalanced datasets, particularly the scarcity of anomalous samples. This paper introduces an innovative data-driven framework for pipeline condition recognition by transforming tabular operational data into standardized image representations, mapping condition information directly to pixel points to enable uniform input for deep learning models. To address the real-world challenge of limited anomalous samples, practical scenarios are simulated using a Conditional Wasserstein Generative Adversarial Network (CWGAN) for targeted image-based data augmentation, generating balanced datasets while validating synthetic samples against originals through metrics such as KL divergence to ensure distributional similarity and feasibility. After in-depth analysis of the augmented data's impact on model robustness, an enhanced Convolutional Neural Network (CNN) incorporating parallel convolution kernels and self-attention mechanisms is developed to extract multi-scale features and output condition probabilities. Eventually, several simulated cases mimicking real-world operational imbalances with scarce anomalous samples are used for model verification and performance comparisons. Combined with CWGAN augmentation, the effects of different CNN architectures on identification metrics are tested. The results demonstrate that the proposed framework achieves more accurate recognition than other methods, with a 98% overall accuracy, a 25% improvement in F1 -score, and increases in precision and recall to over 0.95. It is suggested that the proposed framework excels in handling imbalanced and heterogeneous data while capturing complex operational patterns and nonlinear dependencies, providing technical support for the efficient management of refined oil pipelines.

Keywords: Refined Oil, Pipeline Condition Identification, Image transformation, CWGAN augmentation, Enhanced CNN.