Data-driven predictive maintenance of induction motors using self-supervised and federated learning on noisy current and vibration signals
) differential privacy. Performance remains stable under Dirichlet non-IID distributions (α = 0.1), confirming practical robustness to heterogeneous multi-site data. By coupling unlabelled representation learning with confidentiality-aware collaboration and severity-aware evaluation, the method advances a practical path to scalable, noise-robust, and compliant condition monitoring for Industry 4.0 assets.
Authors
- T. Gopalakrishnan (ORCID: https://orcid.org/0000-0003-0923-4076)
- Choon Kit Chan (ORCID: https://orcid.org/0000-0001-7478-7334)
- N. Shanmugasundaram
- Saurav Dixit (ORCID: https://orcid.org/0000-0002-6959-0008)
- Chandrakant Sonawane
- Deepa Simon
- Arunkumar Bongale
Institutions
- INTI International University (MY)
- Vels University (IN)
- Symbiosis International University (IN)
- Iraqi University (IQ)
- Chitkara University (IN)
- Saveetha University (IN)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1038/s41598-026-46550-6
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00