Online diagnosis for sucker rod pumping well has great significances for rapidly grasping operations of the oil well. Feature extraction of the working condition and determination of the online diagnostic algorithm are two indispensable parts. In this paper, five feature vectors are extracted using Freeman chain codes. Then, an optimized density peak clustering (DPC) method is proposed to realize online diagnosis solved by an improved brain storm optimization (BSO) algorithm, in which the cloud model is adopted to generate new solutions in the searching space. During the online diagnosis process, a new cluster updating strategy is used to update the cluster centers online. According to the proposed online diagnostic method, various samples are automatically classified into different classifications by the unsupervised learning. The simulation results verify that the proposed online diagnosis method is satisfactory, which can give a higher and more stable diagnostic accuracy.

有杆抽油井在线诊断对快速掌握油井作业情况具有重要意义。工况特征提取和在线诊断算法确定是两个不可缺少的部分。在本文中，使用弗里曼链码提取了五个特征向量。然后，提出了一种优化的密度峰值聚类（DPC）方法，通过改进的脑风暴优化（BSO）算法实现在线诊断，该算法采用云模型在搜索空间中生成新的解决方案。在在线诊断过程中，采用新的集群更新策略在线更新集群中心。根据提出的在线诊断方法，通过无监督学习将各种样本自动分类为不同的类别。