Fault Identification of Electric Submersible Pumps Based on Unsupervised and Multi-Source Transfer Learning Integration

Peihao Yang, Jiarui Chen, Lihao Wu and Sheng Li ()
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Peihao Yang: Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China
Jiarui Chen: Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China
Lihao Wu: School of Computer Engineering, Guangzhou City University of Technology, Guangzhou 510800, China
Sheng Li: Faculty of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China

Sustainability, 2022, vol. 14, issue 16, 1-17

Abstract: The ratio between normal data and fault data generated by electric submersible pumps (ESPs) in production is prone to imbalance, and the information carried by the fault data generally as a minority sample is easily overwritten by the normal data as a majority sample, which seriously interferes with the fault identification effect. For the problem that data imbalance under different working conditions of ESPs causes the failure data to not be effectively identified, a fault identification method of ESPs based on unsupervised feature extraction integrated with migration learning was proposed. Firstly, new features were extracted from the data using multiple unsupervised methods to enhance the representational power of the data. Secondly, multiple samples of the source domain were obtained by multiple random sampling of the training set to fully train minority samples. Thirdly, the variation between the source domain and target domain was reduced by combining weighted balanced distribution adaptation (W-BDA). Finally, several basic learners were constructed and combined to integrate a stronger classifier to accomplish the ESP fault identification tasks. Compared with other fault identification methods, our method not only effectively enhances the performance of fault data features and improves the identification of a few fault data, but also copes with fault identification under different working conditions.

Keywords: imbalance data; fault identification; electric submersible pumps (ESPs); unsupervised; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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