近日，由王旱祥教授指导、博士生吕孝孝撰写的论文《Adaptive fault diagnosis of sucker rod pump systems based on optimal perceptron and simulation data 》被“Petroleum Science”正式录用，第一单位为中国石油大学（华东），通讯作者为王旱祥教授。“Petroleum Science”属于工程技术大类2区（中科院基础版分区），2019年11月入选国家七部委“中国科技期刊卓越行动计划”的“领军期刊”项目，主要刊登石油科学领域高质量原创性研究论文和综述性论文。

A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system (SRPS) used in oil wells to ensure a safe and efficient production. The current diagnosis method is pattern recognition of a dynamometer card (DC) based on feature extraction and perceptron. The premise of this method is that the training and target data have the same distribution. However, the training data are collected from a field SRPS with different system parametersdesigned to adapt to production conditions, which may significantly affect the diagnostic accuracy. To address this issue, in this study, an improved model of the sucker rod string (SRS) is derived by adding fault-parameter dimensions, with which DCs under 16 working conditions could be generated.  Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data. Meanwhile, to further improve the accuracy of the proposed method, the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples. The parameters of the perceptron are optimized to promote its discriminability. Finally, the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.