基于CNN-LSTM的复合神经网络在油田污水系统故障诊断中的应用  

Application of Composite Neural Network Based on CNN-LSTM in Fault Diagnosis of Oilfield Wastewater System

作　　者：钟艳 ZHONG Yan(Fourth Oil Production Plant Digital Operation and Maintenance Center,Daqing Oilfield,Daqing 163318,China)

机构地区：[1]大庆油田第四采油厂数字化运维中心,黑龙江大庆163518

出　　处：《吉林大学学报（信息科学版）》2024年第5期817-828,共12页Journal of Jilin University（Information Science Edition）

基　　金：海南省自然科学基金资助项目(623MS071)。

摘　　要：为提高油田污水系统故障诊断的智能化水平和准确性,利用卷积神经网络以及长短期记忆网络构建复合神经网络,并采用Adam与随机梯度下降法对结构进行优化,使模型收敛速度以及故障诊断精度得到提升。通过相关实验研究结果表明,采用的优化算法使模型准确度提升至0.87左右,模型诊断损失率降至0.032左右;复合神经网络结构的平均检测精度达到0.888,准确值达到0.883,召回率达到0.789。将复合神经网络应用于油田污水系统故障诊断中,使油田污水系统实现智能故障检测,并能降低经济成本,益于智慧油田建设。This study aims to improve the intelligence and accuracy of fault diagnosis in oilfield wastewater systems.A composite neural network is constructed using convolutional neural networks and long short-term memory networks,and the structure is optimized using Adam and random gradient descent method to improve the convergence speed and fault diagnosis accuracy of the model.The study is validated through relevant experiments,and the experimental results show that the optimization algorithm used in the study improves the accuracy of the model to around 0.87 and reduces the diagnostic loss rate of the model to around 0.032.The average detection accuracy of the composite neural network structure reaches 0.888,with an accuracy value of 0.883 and a recall rate of 0.789.The composite neural networks is applied to fault diagnosis of oilfield wastewater systems,can achieve intelligent fault detection,reduce economic costs,and build smart oilfield.

关 键 词：卷积神经网络-长短期记忆 复合神经网络 污水系统 故障检测 随机梯度下降法 智慧油田 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]