Enhancing Small-Sample Fault Data in Pipelines: Multi-Constrained GAN Optimization

Natural gas pipeline fault data has the characteristics of sample scarcity, class imbalance, non-stationarity and weak transient features, which seriously restricts the performance of data-driven intelligent monitoring models. These technical bottlenecks are not only engineering problems, but also pose challenges to social public safety, energy supply stability and risk management of critical infrastructure. This study systematically analyzes the potential of generative adversarial networks (GANs) to enhance pipeline fault data under small sample conditions. The results show that traditional threshold alarm mechanisms have difficulty in processing complex time series signals, while GAN variants (such as CGAN, DCGAN, WGAN-GP) can synthesize high-fidelity virtual samples by learning fault distribution, effectively expand the training dataset and alleviate class imbalance. In order to deal with the difficulties of strong temporal dependence, multi-channel fusion and weak transient features of pipeline signals, it is crucial to integrate time modeling (such as TimeGAN, RCGAN), multi-scale discriminators and optimization strategies with physical consistency constraints. Future work should focus on verifying the physical rationality of synthetic samples and exploring multimodal collaborative generation fused with domain knowledge to improve the robustness and accuracy of fault identification, and ultimately provide decision support for building a more resilient and predictive intelligent pipeline monitoring system.