基于误差补偿的GM-RBF海底管道腐蚀预测模型  

GM-RBF model based error compensation for prediction of submarine pipeline corrosion

作　　者：骆正山[1] 袁宏伟 LUO Zhengshan;YUAN Hongwei(School of Management, Xi＇an University of Architecture and Technology, Xi＇an Shaanxi 710055, China)

机构地区：[1]西安建筑科技大学管理学院

出　　处：《中国安全科学学报》2018年第3期96-101,共6页China Safety Science Journal

基　　金：国家自然科学基金资助(61271278);陕西省重点学科建设专项资金资助(E08001);陕西省教育厅专项基金资助(16JK1465)

摘　　要：为准确预测海底油气管道腐蚀剩余寿命,构建基于误差补偿原理的灰色径向基函数（GM-RBF）神经网络腐蚀速率预测模型。首先,建立腐蚀速率的灰色模型（GM）,将腐蚀速率灰色预测值作为径向基（RBF）神经网络的输入,残差作为输出,训练神经网络得到误差补偿器;其次,补偿新的灰色预测值,得到腐蚀速率的最终预测值;然后,根据预测结果计算出年腐蚀深度,结合剩余强度准则,计算管道剩余寿命;最后,以某海底管道为实例,验证模型的预测有效性。结果表明：单一使用GM模型预测的相对误差为17.48%,用GM-RBF模型预测的相对误差为6.37%,并预测出管道的剩余寿命为5.4年,GM-RBF模型提高了预测精度,且能够较好地描述腐蚀发展趋势。In order to accurately predict the residual life of submarine oil and gas pipelines subject to corrosion,a GM-RBF neural network corrosion rate prediction model based on error compensation principle was built. Firstly,a gray model（GM） of corrosion rate was established,and the gray prediction value of corrosion rate was taken as the input to radial basis（RBF） neural network,and the residual was taken as the output. The neural network was trained to obtain the error compensator. The new grey prediction value was compensated,and the final prediction value of the corrosion rate was obtained. The annual corrosion depth was calculated according to the prediction results,and the residual service life of the pipeline was calculated in combination with the residual strength criterion. The effectiveness of the model was verified by using the data on a certain subsea pipeline take as an example. The results show that the relative error of single-use GM model prediction is 17. 48%,the relative error predicted by GM-RBF model is 6. 37%,and the remaining life of the pipeline is predicted to be 5. 4 years,and that the GM-RBF model improves the prediction accuracy and can make a better description of the trend in the corrosion development.

关 键 词：海底油气管道 腐蚀剩余寿命 误差补偿 灰色径向基函数(GM-RBF) 腐蚀速率 

分 类 号：X937[环境科学与工程—安全科学]