抽油机故障诊断的分布驱动主动学习算法  

Distributed Drive Active Learning Algorithm for Fault Diagnosis of Pumping Unit

作　　者：汪敏 周磊 闵帆[2] 张响 沈佳园 韩菲 WANG Min;ZHOU Lei;MIN Fan;ZHANG Xiang;SHEN Jiayuan;HAN Fei(School of Electrical Information,Southwest Petroleum University,Chengdu 610500,China;School of Computer Science,Southwest Petroleum University,Chengdu 610500,China;Zhejiang Zheneng Natural Gas Operation Co.,Ltd.,Hangzhou 310052,China;Fengcheng Factory,Xinjiang Oil Field,Karamay 834000,China)

机构地区：[1]西南石油大学电气信息学院,成都610500 [2]西南石油大学计算机科学学院,成都610500 [3]浙江浙能天然气运行有限公司,杭州310052 [4]新疆油田公司风城油田,克拉玛依834000

出　　处：《南京航空航天大学学报》2022年第3期517-527,共11页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家自然科学基金(62006200);四川省科技计划支持项目(2020YFQ0038,22ZDYF2733)。

摘　　要：抽油机示功图直观显示了抽油机工作情况,但实际工况情况呈现典型的长尾分布特性,类别严重不平衡。传统方法无法准确识别小类别工况,也无法获得井下工作状态准确识别。针对这一问题,提出一种基于分布驱动的多类别长尾数据代价敏感主动学习算法(Cost-sensitive active learning algorithm based on distribution-driven multi-class long-tailed data,CALA)。首先,考虑数据分布特性,以最小化代价为优化目标确定数据的最佳聚类簇数;其次,通过加入预分类误差代价来更新之前得到的最佳聚类簇数;然后,构建集成分类模型作为分类器;最后,通过迭代来平衡数据分布。采用某油田真实的示功图数据进行测试,显著性实验分析证明CALA在小类别工况诊断上具有更好的性能。The indicator diagram of the pumping unit visually shows the working conditions of the pumping unit.However,the actual working conditions show typical long-tailed distribution characteristics,and the categories are seriously unbalanced.Traditional methods cannot accurately identify small categories of working conditions,and cannot obtain accurate identification of underground working conditions.Aiming at this problem,a cost-sensitive active learning algorithm based on distribution-driven multi-class long-tail data(CALA)is proposed.First,considering the characteristics of data distribution,the optimal number of clusters for the data is determined by minimizing the cost as the optimization objective.Second,the optimal number of clusters obtained before is updated by adding the pre-classification error cost.Then,a classifier is constructed by integrating the classification models.Finally,balance the data distribution iteratively.Using the real indicator diagram data of an oil field to test,the significant experimental analysis proves that CALA has better performance in the diagnosis of small categories of working conditions.

关 键 词：示功图诊断 代价敏感 主动学习 长尾分布 小类别工况识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]