Integrated Transfer and Self-Supervised Learning for Data-Efficient Bearing Fault Diagnosis: A Survey Toward Industry 5.0

Abstract

Rolling bearing fault diagnosis remains constrained by three persistent challenges arising from limited labeled fault data, distribution shifts across machines and operating conditions, and privacy restrictions on centralized data collection. Transfer learning (TL) and self-supervised learning (SSL) address these challenges in complementary ways. TL facilitates knowledge transfer across related domains and tasks, whereas SSL learns transferable representations from abundant unlabeled vibration and acoustic signals. This survey provides a unified review of TL and SSL methods for bearing fault diagnosis, organized according to learning settings, transfer strategies, deployment objectives, benchmark datasets, and evaluation protocols. Recent developments are further categorized into three integration paradigms, namely self-supervised transfer (SST) learning, semi-supervised domain adaptation (SSDA), and federated TL–SSL, and their potential to support privacy-preserving, resource-efficient, and interpretable fault diagnosis in Industry 5.0 environments is examined. The synthesis establishes several quantified trends across the reviewed literature: SSL pretraining enables diagnostic accuracy comparable to fully supervised baselines with only 5–15% of the labels, federated TL–SSL foundation models operate competitively with as little as 1% labeled data, and source-free domain adaptation recovers 82–91% accuracy under domain shift without access to source data. At the same time, the analysis indicates that existing studies frequently investigate TL and SSL independently, rely predominantly on controlled benchmark datasets, and provide limited validation of cross-machine and cross-condition generalization. Key research challenges are subsequently identified, including reliable transfer under nonstationary conditions, label noise and class imbalance, computational efficiency, uncertainty quantification, and model explainability. Finally, a research agenda is presented for developing scalable, trustworthy, and deployment-ready rolling bearing fault diagnosis systems.