As the main means of energy transportation, pipelines have the characteristics of 24 h uninterrupted transportation, low cost, strong transmission capacity, and low risk, and they have become an important device for natural gas transportation. However, due to defects caused by the pipeline itself and external forces, it is easy for natural gas leakage to cause major accidents, serious environmental pollution, and huge losses of life and property. Traditional pipeline defect detection technology can be interfered with by a large number of noise signals when collecting defect signals, and the noise reduction method is simple but has poor accuracy, so it cannot directly obtain effective pipeline defect information from the signal and cannot effectively improve the emergency intensity and emergency level of pipeline operation and maintenance management. Therefore, a new noise reduction method, ICEEMDAN-LMS, for natural gas pipelines is proposed in this paper, and the method is calculated and tested in detail. The effectiveness of the new method is verified through the analysis of a pipeline defect signal and normal signal. Moreover, the proposed method can reduce noise more effectively than conventional methods, which has clear value in engineering applications. Importantly, this noise reduction method not only provides a reliable basis for the intelligent diagnosis of pipeline defect signals, but can also be an important reference for helping management departments to make decisions and emergency plans and formulate on-site treatment programs.

Accurate prediction of the internal corrosion rate is crucial for the safety management and maintenance planning of oil and gas pipelines. However, this task is challenging due to the complex, multi-factor nature of corrosion and the scarcity of available inspection data. To address this, we propose a novel hybrid prediction model, GM-Markov-PSO, which integrates a gray prediction model with a Markov chain and a particle swarm optimization algorithm. A key innovation of our approach is the systematic incorporation of symmetry principles—observed in the spatial distribution of corrosion factors, the temporal evolution of the corrosion process, and the statistical fluctuations of monitoring data—to enhance model stability and accuracy. The proposed model effectively overcomes the limitations of individual components, providing superior handling of small-sample, non-linear datasets and demonstrating strong robustness against stochastic disturbances. In a case study, the GM-Markov-PSO model achieved prediction accuracy improvements ranging from 0.93% to 13.34%, with an average improvement of 4.51% over benchmark models, confirming its practical value for informing pipeline maintenance strategies. This work not only presents a reliable predictive tool but also enriches the application of symmetry theory in engineering forecasting by elucidating the inherent order within complex corrosion systems.

Wax deposition in waxy crude oil pipelines reduces the effective flow area, increases pumping pressure, and may ultimately cause blockage or rupture. Accurate prediction of the wax deposition rate is therefore essential for safe operation and cost-effective maintenance planning. However, existing data-driven models are typically trained on very limited experimental datasets and often rely on single learning algorithms, which restricts their generalization ability in complex operating conditions. To address these challenges, we propose an integrated prediction framework that combines data augmentation using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and a heterogeneous Stacking ensemble model. Starting from 85 experimental samples collected from a crude oil pipeline in the Qinghai oil field, WGAN-GP is trained to generate high-fidelity synthetic data under physicochemical constraints, expanding the training set to 1,060 samples. Comparative analysis against alternative augmentation methods (Gaussian noise, SMOGN, and standard GAN) demonstrates that WGAN-GP achieves superior data quality metrics (coverage ratio: 1.658; NNDR: 0.82) and yields statistically significant improvements in predictive performance (p < 0.05), justifying its additional computational cost. A two-layer Stacking ensemble is then constructed using Random Forest, XGBoost, and Support Vector Regression as base learners and Ridge Regression as the meta-learner. On the held-out test set containing only real samples, the proposed framework achieves a coefficient of determination R² of 0.9632, a mean absolute error (MAE) of 0.4185, and a root-mean-squared error (RMSE) of 0.5272. Compared with the best single model, Support Vector Regression, MAE and RMSE are reduced by 2.58% and 2.35%, respectively, while the MAE is reduced by 10.56% relative to the Random Forest baseline. These results demonstrate that integrating WGAN-GP-based data augmentation with a heterogeneous Stacking ensemble presents a promising framework that can serve as a complementary tool for wax deposition rate prediction and pipeline risk assessment in data-scarce industrial scenarios.

Accurately predict the external corrosion rate is crucial for the integrity management and risk assessment of buried pipelines. However, existing prediction models often suffer from limitations such as low accuracy, instability, and overfitting. To address these challenges, this study proposes a novel hybrid model, FA-IDBO-KELM. Firstly, Factor Analysis (FA) was employed to reduce the dimensionality of ten original corrosion-influencing factors, extracting seven principal components to mitigate multicollinearity. Subsequently, the hyperparameters (penalty coefficient C and kernel parameter γ) of the Kernel Extreme Learning Machine (KELM) were optimized using an Improved Dung Beetle Optimizer (IDBO). The IDBO included four key enhancements compared to the standard DBO: spatial pyramid mapping (SPM) for population initialization, a spiral search strategy, Lévy flight, and an adaptive t-distribution mutation strategy to prevent premature convergence. The model was validated using a dataset from the West–East Gas Pipeline, with 90% of the data being used for training and 10% for testing. The results demonstrate the superior performance of FA-IDBO-KELM, which achieved a root mean square error (RMSE) of 0.0028, a mean absolute error (MAE) of 0.0021, and a coefficient of determination (R²) of 0.9954 on the test set. Compared to benchmark models (FA-KELM, FA-SSA-KELM, FA-DBO-KELM), the proposed model reduced the RMSE by 93.0%, 89.1%, and 85.3%, and improved the R² by 85.7%, 10.6%, and 7.4%, respectively. The FA-IDBO-KELM model provides a highly accurate and reliable tool for predicting the external corrosion rate, which can significantly support pipeline maintenance decision-making.