Federated learning for decentralized fault diagnosis of a sucker-rod pumping system with class imbalance data

Abstract

The development of modern oilfields has entered the middle and late stages, transforming towards digitalization and intelligence. However, the distribution of the sucker-rod pumping systems is decentralized, and the working condition information is skew-distributed. This situation poses a significant challenge to existing centralized fault diagnosis mechanisms. To address the existing practical challenge in the oilfield, a federated learning-based fault diagnosis framework for class imbalance in decentralized sucker-rod pumping systems (FL-CI) is proposed. This framework incorporates a parameter anonymization-ratio upload mechanism to mitigate the risk of gradient tracking. Then, a monitoring mechanism is leveraged to reversely infer global class-imbalance data using trained parameters uploaded by the clients. In addition, a ratio loss function is designed to calibrate the influence of class imbalance on the global system. After conducting comparative analysis, ablation analysis, and sensitivity analysis on a rod-pumping unit dataset (RPUD), as well as comparative and ablation analyses on the Case Western Reserve University bearing dataset (CWRU), the experimental results demonstrate that the FL-CI framework achieves superior diagnostic performance on the RPUD, with eight out of twelve evaluation metrics significantly outperforming seven state-of-the-art methods. A similar trend is observed on the CWRU, further validating the effectiveness and generalizability of the FL-CI.