|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.