Semi-Supervised Graph Attention Network for Screw Pump Fault Diagnosis: Revealing the Dynamic Coupling of Multi-Source Information

TL;DR

This paper introduces a new method for diagnosing screw pump faults by leveraging dynamic coupling of multi-source data using a semi-supervised graph attention network.

Contribution

The novel contribution is the integration of semi-supervised learning with graph attention networks to exploit dynamic coupling in screw pump fault diagnosis.

Findings

The SSL-GAT method effectively captures dynamic coupling between multi-source information.

The proposed method outperforms existing approaches in screw pump fault diagnosis.

Semi-supervised learning reduces the need for large amounts of labeled data.

Abstract

The screw pump is a critical device for elevating downhole petroleum to the surface, and screw pump failure can significantly disrupt the production of oil wells. Due to the complex structure of the screw pump, the same pump fault can cause different changes in the monitoring parameters, and different faults can also cause the same parameter change. In consequence of the complexity, it requires a large amount of labeled data for a diagnosis model to achieve fault diagnosis of a screw pump in practical application. Aiming for this kind of condition, we discovered the dynamic coupling effect between multi-source information through detailed research on the collected data of screw pumps. To fully leverage the information dynamic coupling (IDC) effect, a semi-supervised learning graph attention network (SSL-GAT) fault diagnosis method is proposed. This approach integrates the semi-supervised learning framework and graph attention neural network for the fault diagnosis of a screw pump. The experimental validation of the SSL-GAT method demonstrates its outstanding performance in screw pump fault diagnosis.

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