Considering low accuracy of the machine learning methods in diagnosing the leaks in oilfield water injection pipelines,having the data-driven model plus transfer learning method based to propose a method for diagnosis of the leakage in water injection pipeline networks was implemented.The results show that,the Epanet software can simulate the leakage fault based on the known fault data to realize data en-hancement.After pre-training and secondary training of the transfer learning,comparing the accuracy of data-driven models indicates that,among the five models,the convolutional neural network(CNN)model is the best solution,and the accuracy of leakage diagnosis of water filling pipe network can reach 94.12%.
关键词
注水管网/泄漏诊断/数据驱动模型/迁移学习/卷积神经网络/数据增强
Key words
water injection pipeline network/leakage diagnosis/data-driven model/transfer learning/CNN/data enhancement
维普
万方数据