改进原型网络法诊断抽油机故障  

Improved prototypical network for fault diagnosis of pumping unit

作　　者：何金强 陈朋[1] 赵霖 苗凯 HE Jinqiang;CHEN Peng;ZHAO Lin;MIAO Kai(School of Electrical and information Engineering,Northeast Petroleum University,Daqing 163318,Heilongjiang,China;Technical Supervision Center,PetroChina Daqing Oilfield Co.,Ltd.,Daqing 163711,Heilongjiang,China)

机构地区：[1]东北石油大学电气信息工程学院 [2]中国石油大庆油田有限责任公司技术监督中心

出　　处：《石油钻采工艺》2023年第3期312-318,共7页Oil Drilling & Production Technology

基　　金：黑龙江省高校基本科研业务费项目“基于DAS分布式网络滤波技术研究及页岩油井剖面解释”(编号:2022TSTD-04)。

摘　　要：示功图是判断有杆抽油机工作状况的重要方法。常见故障工况因数据量多使其诊断准确率可达到99%,不常见的故障数据稀少,导致诊断准确率仅有90%~92%。为了提高数据量较少时抽油机故障诊断准确率,提出了基于压缩激励模块(Squeezeand-Excitation)改进原型网络的抽油机故障诊断方法。首先引入残差连接以及注意力模块对示功图特征进行提取,然后将不同类别的示功图像映射到特征空间,经过度量距离后输入Softmax分类器,实现小样本条件下抽油机故障诊断。研究结果表明,改进的原型网络模型在不同小样本数据集上分类准确率均提高。尤其在样本数据图小于50张时,比AlexNet及ResNet34模型对正常、供液不足、泵上碰、气影响、游动阀漏失、砂阻等6种故障类型诊断准确率提高3%~20%。The indicator diagram is an important method to judge the working condition of rod pumping unit.Due to the large amount of data,the diagnosis accuracy of common fault conditions can reach 99%,while the diagnosis accuracy of a small part of the fault data is only 90%-92%.To improve the fault diagnosis accuracy of pumping unit when the amount of data is small,an improved prototypical network based on the Squeeze and Excitation module is proposed for fault diagnosis of pumping unit.The residual connection and the Squeeze-and-excitation module are introduced to extract the characteristics of the indicator diagram.Then the indicator diagram images of different categories are mapped to the feature space.After measuring the distance,input the Softmax classifier to realize the fault diagnosis of the pumping unit under the condition of small sample.The experimental results show that the improved prototypical network model can improve the classification accuracy on different small sample data sets.Especially when the sample data is less than 50,the diagnostic accuracy of the six fault types of normal,insufficient liquid supply,pump collision,gas influence,floating Vanal leakage and sand resistance is increased by 3%-20%compared with AlexNet and ResNet34 models.

关 键 词：油气资源 非常规油气 勘探开发 工程技术 抽油机 示功图 原型网络 注意力模块 故障诊断 

分 类 号：TE937[石油与天然气工程—石油机械设备] TP18[自动化与计算机技术—控制理论与控制工程]