基于PSO-GRNN模型的埋地管道腐蚀剩余寿命预测

目的 构建埋地管道腐蚀深度预测模型，预测腐蚀管道的剩余使用寿命。方法 依据ASME B31G剩余强度评价标准，给出管道的最大允许腐蚀深度计算方法，引入广义回归神经网络（GRNN），构建埋地管道腐蚀深度预测模型，采用粒子群算法（PSO）优化GRNN的网络参数，结合管道腐蚀发展趋势预测方法，对埋地薄弱管道进行腐蚀剩余寿命预测。以陕西省某埋地输油管道为例，选取8个主要外腐蚀因素，构建外腐蚀指标体系，借助Pycharm编程仿真，结合埋片试验，对该模型预测结果进行验证分析，并预测各腐蚀管段剩余使用寿命。结果 与BP模型相比，PSO-GRNN模型的管道腐蚀深度预测结果最大相对误差控制在13.77%以内，平均相对误差仅为6.63%。寿命预测结果显示，部分管段的剩余使用寿命未能达到其预期服役寿命。结论 所建模型预测性能要明显优于BP模型，预测精度更高，能够较好地预测埋地管道的最大腐蚀深度和未来的腐蚀发展规律，剩余寿命预测结果贴近实际，为管道的维修和更换提供了指导依据，在实际工程中，具有一定的应用价值。

Prediction on Remaining Service Life of Buried Pipeline after Corrosion Based on PSO-GRNN Model

The work aims to construct a prediction model for the corrosion depth of buried pipeline and predict the remaining service life of the corroded pipeline. According to the ASME B31G residual strength evaluation standard, the maximum allowable corrosion depth calculation method of pipeline was given. The generalized regression neural network (GRNN) was introduced to construct the buried pipeline corrosion depth prediction model, and the particle swarm optimization (PSO) algorithm was used to optimize the GRNN network parameters. Combined with the prediction method of pipeline corrosion development trend, the residual life of buried weak pipelines after corrosion was predicted. With a buried oil pipeline in Shaanxi Province as the example, eight major external corrosion factors were selected to construct an external corrosion index system. With the help of Pycharm programming simulation and buried chip test, the prediction results of the model were verified and analyzed, and the remaining service life of corroded sections was predicted. Compared with the BP model, the maximum relative error of the pipeline corrosion depth predicted by the PSO-GRNN model was controlled within 13.77%, and the average relative error was only 6.63%. From the prediction on service life, the remaining service life of some sections failed to reach the expected value. The prediction performance of the prospected model is obviously better than that of BP model. The prediction accuracy is higher, and the maximum corrosion depth and future corrosion development law of buried pipeline can be better predicted. The prediction result of remaining life is close to the actual value, which provides guiding basis for maintenance and replacement of pipeline and has certain application value in actual engineering.