Deep Learning-driven Predictive Maintenance For Artificial Yarn Machine With Real-time Iot Deployment

Abstract

Predictive Maintenance Has Emerged As A Critical Requirement In Modern Textile Manufacturing To Minimize Machine Downtime And Improve Operational Efficiency. This Research Presents A Deep Learning–based Predictive Maintenance System For Artificial Yarn Machines Integrated With Real-time IoT Deployment. The System Collects Sensor Data Such As Temperature, Vibration, And Operational Load From Yarn Machines Continuously. Advanced Data Preprocessing Techniques Are Applied To Clean And Structure The Incoming Data Stream. A Deep Learning Model Is Trained To Identify Hidden Patterns Associated With Machine Failures. The Proposed System Predicts Potential Faults Before They Occur, Enabling Proactive Maintenance. Real-time Monitoring Is Achieved Through IoT Devices Connected To A Centralized Analytics Platform. The System Reduces Unexpected Breakdowns And Enhances Machine Lifespan. It Also Improves Production Quality And Consistency In Yarn Manufacturing. The Model Is Evaluated Using Multiple Performance Metrics To Ensure Reliability. Experimental Results Demonstrate Improved Prediction Accuracy Compared To Traditional Methods. The Solution Is Scalable And Suitable For Industrial Deployment. This Work Contributes To Intelligent Manufacturing By Integrating AI And IoT Technologies Effectively.

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