An Analysis of Semi-Supervised Machine Learning in Electrical Machines

This research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate characteristics of electrical machines. SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliability, and maintenance by leveraging labeled and unlabeled data. The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. Moreover, the document discusses the specific difficulties associated with this merger and offers remedies to improve the utilization of SSML in this significant area. A detailed table summarizes various methods and emphasizes their role in furthering machine learning within the realm of electrical machines.