Predictive Maintenance and Energy Optimization with AI-Driven IoT Framework in Textile Manufacturing Industry

Predictive Maintenance and Energy Optimization with AI-Driven IoT Framework in Textile Manufacturing Industry Kathirvel M. Chandrasekaran M.

The textile industry is rapidly automating, yet frequent machine failures and excessive energy consumption continue to impede efficiency. Predictive analytics and AI-driven energy management are critical in overcoming these challenges. This study presents an Adaptive Deep Reinforcement Learning with Bayesian Optimization (ADRL-BO) model, integrating predictive maintenance with IoT-based energy control to enhance operational reliability. The framework aims to reduce unexpected equipment failures and optimize energy consumption using real-time AI analytics. Data is collected from major textile hubs in India, including Surat, Coimbatore, and Ludhiana, covering 500+ industrial machines. Key machine parameters, such as acoustic signals, thermal fluctuations, and vibrations, are monitored through IoT sensors. The ADRL-BO model utilizes deep reinforcement learning (DRL) for adaptive fault detection, while Bayesian optimization refines maintenance scheduling. Additionally, an IoT-driven smart grid dynamically manages power distribution, adjusting motor speeds and compressor loads based on real-time demand. Blockchain technology ensures secure, transparent data logging of energy usage. Ultra-fast 5G IoT communication supports seamless data exchange for real-time analytics. Evaluation results demonstrate a 45% reduction in downtime and 35% energy savings, validating ADRL-BO’s effectiveness over conventional AI methods in achieving a more sustainable and intelligent textile manufacturing ecosystem.
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