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

Abstract

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.