Python Machine Learning - Analysis of Semi-Supervised Machine Learning - ClickMyProject

This research explores semi-supervised learning approaches to improve fault diagnosis and performance monitoring in electrical machines, where labeled data is limited. The study applies algorithms that leverage both labeled and unlabeled data to enhance model accuracy. Results demonstrate better detection rates compared to purely supervised or unsupervised methods. The approach supports predictive maintenance and operational efficiency. Challenges in industrial implementation and data quality are addressed. Findings suggest semi-supervised learning as a practical solution for smart manufacturing.


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