Transform Electric Power Curve into Dynamometer Diagram Image Using Deep Recurrent Neural Network

Junfeng SHI  Wenming MA  Peng SONG  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.8   pp.2154-2158
Publication Date: 2018/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8027
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
deep learning,  embedding,  recurrent neural network,  computer vision,  

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To learn the working situation of rod-pumped wells under ground, we always need to analyze dynamometer diagrams, which are generated by the load sensor and displacement sensor. Rod-pumped wells are usually located in the places with extreme weather, and these sensors are installed on some special oil equipments in the open air. As time goes by, sensors are prone to generating unstable and incorrect data. Unfortunately, load sensors are too expensive to frequently reinstall. Therefore, the resulting dynamometer diagrams sometimes cannot make an accurate diagnosis. Instead, as an absolutely necessary equipment of the rod-pumped well, the electric motor has much longer life and cannot be easily impacted by the weather. The electric power curve during a swabbing period can also reflect the working situation under ground, but is much harder to explain than the dynamometer diagram. This letter presented a novel deep learning architecture, which can transform the electric power curve into the dimensionless dynamometer diagram image. We conduct our experiments on a real-world dataset, and the results show that our method can get an impressive transformation accuracy.