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Abstract
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This paper introduces Rotary Insight, an open-source, user-friendly framework for bearing fault diagnosis and health monitoring in rotary machines. The platform provides an intuitive interface for interacting with time-series vibration data, enabling users to upload, segment, and classify faults easily. It automatically preprocesses data, segments it, and provides predictions, including fault classifications and visualizations such as spectrograms. Aimed primarily at educational and research purposes, Rotary Insight also provides a modular environment for experimenting with various models and datasets, making it a valuable tool for predictive maintenance. Future work will focus on improving scalability and knowledge transfer for broader industrial use.
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