A Reliability Prediction Method for Pipelines with Corrosion Defects Based on Gaussian Mixture Model

Abstract

The assessment of pipeline corrosion rate is a critical component in the prediction of pipeline reliability. Traditional methods for predicting the reliability of corroded pipelines often suffer from excessive conservatism and insufficient accuracy in corrosion rate estimation. To address these issues, this paper proposes a reliability prediction methodology for pipelines with corrosion defects based on the Gaussian Mixture Model (GMM). First, lever-aging pipeline corrosion inspection data, the GMM is employed to fit the probability distribution of pipeline corrosion rates, thereby establishing a model that captures the distribution patterns of corrosion rates. Subsequently, a pipeline reliability assessment model is developed based on this distribution pattern, with the 95th percentile of the distribution model selected as the predicted corrosion rate. Finally, by integrating the reliability assessment model with the corrosion rate, the future reliability of the pipeline is predicted. Experimental results demonstrate that the deep Gaussian Mixture Model effectively fits the distribution of pipeline corrosion rates, with a K-S test value as low as 0.1295, outperforming traditional probability distribution models. Comparative analysis of prediction results from typical engineering cases indicates that the proposed methodology aids in formulating more scientific and rational maintenance strategies, thereby enhancing the operational efficiency of pipelines. This approach not only improves the accuracy of corrosion rate estimation but also provides a robust framework for reliability prediction, contributing to the optimization of pipeline management practices