基于改进TCN的上扣扭矩序列数据分类  

Classification of make-up torque sequence data based on improved TCN

作　　者：邓智 王正勇[1] 何小海[1] 滕奇志[1] 何海波 Deng Zhi;Wang Zhengyong;He Xiaohai;Teng Qizhi;He Haibo(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China;Chengdu Xitu Technology Co.,Ltd.,Chengdu 610024,China)

机构地区：[1]四川大学电子信息学院,成都610065 [2]成都西图科技有限公司,成都610024

出　　处：《电子测量技术》2024年第18期1-8,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(62071315)项目资助。

摘　　要：在油气开发领域,油套管安装后的密封性能检测尤为重要。其中,上扣过程中产生的扭矩序列数据可以作为油套管密封性的评判依据,用来判断上扣是否合格。为了利用上扣扭矩序列数据信息进行油套管密封性的识别分类,首先基于TCN网络模型结构,再融入位置编码机制和自注意力机制,搭建了一种新的网络模型,即PSE-TCN网络。通过比较不同策略下的结果准确率,展示了模型学习的过程,通过与其他网络模型进行对比,验证了本方法的有效性。实验结果表明,PSE-TCN相较于其他经典网络模型和一些改进后的TCN网络模型,扭矩序列识别精度有较大提升,在自制UCR_whorl数据集上,模型识别准确率达到93.41%。In the field of oil and gas development,the sealing performance test of oil casing after installation is particularly important.Torque sequence data is an important basis for judging the sealing performance of the oil casing,which can be used to judge whether the buckle is qualified.In order to identify and classify the sealing performance of the oil casing by using the information of the buckled torque sequence data,a new network model was built which named PSE-TCN network based on the TCN model integrated with position encoding and self-attention mechanisms.By comparing the accuracy of results under different strategies,the learning process of the model was demonstrated.The effectiveness of this method was validated by comparing it with other network models.Experimental results show that torque sequence recognition accuracy was significantly improved by the PSE-TCN network compared with other classical network models and several improved TCN models.The recognition accuracy of this model achieved 93.41%on the self-made UCR_whorl dataset.

关 键 词：上扣扭矩 时间序列分类 位置编码 时间卷积网络 自注意力机制 下采样 

分 类 号：TN911[电子电信—通信与信息系统] TE938[电子电信—信息与通信工程]