发表机构
University of Alberta; University of Shanghai for Science and Technology（阿尔伯塔大学; 上海理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电潜泵故障诊断中类别不平衡与单元差异性问题，提出结合孪生对比学习和先验校正KNN的框架，经LOEO验证，实现稳健的故障分类，支持可靠预测性维护。
AI 中文摘要
电潜泵（ESP）在海上石油生产中至关重要，意外故障可能导致重大的运营和经济损失。由于非线性运行条件、类别不平衡以及泵单元之间的差异性，ESP系统的准确预测性维护仍然具有挑战性。为解决这些问题，本研究提出了一种故障诊断框架，通过采用孪生对比表示学习和先验校正的k近邻（KNN）分类来纳入类别不平衡意识。该方法首先从振动域指标和工程化谐波关系中提取与故障检测相关的判别性特征。训练一个具有类别平衡对比对的孪生神经网络，以构建一个嵌入空间，该空间将相同故障类型的样本聚类，并区分不同故障类别。为进一步缓解分类过程中的类别不平衡，应用了先验校正的距离加权KNN。该框架使用留一ESP（LOEO）策略进行验证，以评估对未见过的ESP单元的泛化能力。实验结果表明，所提出的框架在现实工业条件下提供了稳健且一致的故障分类性能，支持其在可靠预测性维护和智能ESP系统监控方面的潜力。
英文摘要
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 relationships. A Siamese neural network is trained with class-balanced contrastive pairs to construct an embedding space that clusters samples of the same fault type and separates different fault classes. To further mitigate class imbalance during classification, a prior-corrected distance-weighted KNN is applied. The framework is validated using a Leave-One-ESP-Out (LOEO) strategy to evaluate generalization to previously unseen ESP units. Experimental results indicate that the proposed framework delivers robust and consistent fault classification performance under realistic industrial conditions, supporting its potential for reliable predictive maintenance and intelligent ESP system monitoring.
Comments6 pages, 4 figures, 4 tables
