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Machine Learning for Yarn Machine Maintenance by hatir.guu is a document available to read on EtoBox.

This research presents a machine learning-based predictive maintenance (PdM) system for artificial yarn production machines, aimed at enhancing efficiency and reliability by detecting faults using sensor data. The study evaluates various machine learning algorithms, finding that a deep learning model achieved the highest accuracy of 96% in predicting equipment failures. The proposed PdM framework integrates data acquisition, analysis, and real-time monitoring, demonstrating significant improvements over tra

Author

hatir.guu

Language

EN