Self-supervised learning for predictive maintenance
Status:
active
Start date:
2021-03-01
End date:
2026-03-01
| Machine learning has been widely used in predictive maintenance to learn to predict potential failures of machinery equipment or systems using previous data records. Currently various supervised learning techniques are being exploited in this area. However, they all require labelled training data, which are highly expensive to acquire. Moreover, the batch-mode of supervised learning does not account for dynamic properties and therefore cannot adapt to drifting conditions of the equipment or systems of interest. This project will develop self-supervised and continual learning methods to promote wider accessibility to data-driven predictive maintenance in power networks. The feature of continual (and life-long) learning is of high merit to support more informed and accurate maintenance decisions by handling evolving conditions of power networks such as aging effects of electrical components. Case studies with data collected from power stations will be performed to evaluate the efficacy of the proposed method. |
| First Name | Last Name | Title |
|---|---|---|
| Ning | Xiong | Professor |
| Elaine | Åstrand | Associate Professor,Senior Lecturer |
| Sarala | Mohan | Industrial Doctoral Student |
Ensemble-Based Self-Supervised Meta-Learning for Anomaly Detection in Power Systems (Jul 2026) Sarala Mohan, Pawel M Stano, James Ottewill, Ning Xiong Journal of IEEE Access (IEEE-Access2026)
XES3MaP: Explainable Risks Identified from Ensembled Stacked Self-Supervised Models to Augment Predictive Maintenance (Jun 2024) Sarala Mohan, Ning Xiong IEEE Conference on Artificial Intelligence (CAI2024)
Interval Number-Based Safety Reasoning Method for Verification of Decentralized Power Systems in High-Speed Trains (Jan 2021) Peng Wu, Ning Xiong, Jinzhao Wu Mathematical problems in engineering (MPE21)
| Partner | Type |
|---|---|
| Hitachi AB | Industrial |
