ESP fault diagnosis method based on sparse filtering

Electrical submersible pump (ESP), as a kind of artificial lift device, is widely applied in offshore oilfields due to its strong liquid lifting capability. However, marine environment is complex, there are a variety of ESP faults, and the fault data are lack, so ESP cannot be effectively applied in offshore oilfields, and its application is limited to some extent. The effective on-line diagnosis and quantitative analysis of ESP’s current signals can be hardly realized. To solve these problems, this paper put forward a sparse filtering characteristics extraction method needing rare hyperparameter tuning, which conducts effective characteristics extraction and mode identification on current signals under various working conditions, so as to obtain an accurate and efficient real-time diagnosis model. In addition, its effectiveness was verified by analyzing the field data. It is experimentally indicated that this method can effectively extract characteristics and diagnose 10 kinds of ESP fault states in offshore oilfields with diagnosis accuracy high up to 99.1%. More efficient and accurate fault diagnosis can be realized as the more and more data is acquired and fault types are clarified better.