Contrastive Siamese Representation Learning for Predictive Maintenance of Electrical Submersible Pumps
Electrical submersible pumps (ESPs) are essential in offshore oil production, where unexpected failures can result in significant operational and financial losses. Accurate predictive maintenance for ESP systems remains challenging due to nonlinear operating conditions, class imbalance, and variability among pump units. To address these issues, this study presents a fault diagnosis framework that incorporates class imbalance awareness by employing Siamese contrastive representation learning and prior-corrected k-nearest neighbor (KNN) classification. The method first extracts discriminative features relevant to fault detection from vibration-domain indicators and engineered harmonic relat...
| Authors | Seshu K. Damarla, Xiuli Zhu |
|---|---|
| Primary category | cs.LG |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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