Online fault diagnosis for sucker rod pumping well by optimized density peak clustering

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.