Digital Twin-Enabled Predictive Maintenance for Textile and Mechanical Systems

The increasing digital transformation of industrial manufacturing has intensified the demand for intelligent maintenance strategies capable of minimizing downtime and improving operational reliability. Traditional preventive maintenance approaches, which rely on fixed schedules, often fail to capture real-time equipment health and degradation patterns, particularly in complex textile and mechanical systems. Predictive maintenance addresses this limitation by leveraging operational data to anticipate failures before they occur. In parallel, digital twin technology virtual representations of physical assets continuously synchronized with real-time sensor data has emerged as a powerful tool for enhancing monitoring, analysis, and decision-making. This paper presents a Digital Twin Enabled Predictive Maintenance framework specifically designed for textile and mechanical manufacturing systems. The proposed framework integrates Industrial IoT-based data acquisition, digital twin modeling, and machine learning-driven fault prediction to enable continuous condition monitoring and proactive maintenance planning. By comparing real-time operational data with virtual system behavior, the framework detects early-stage faults, predicts remaining useful life, and optimizes maintenance schedules. Experimental and simulation-based evaluations demonstrate that the proposed approach significantly improves fault detection accuracy, enhances system availability, and reduces maintenance costs when compared with conventional preventive and standalone predictive maintenance methods. The results confirm the effectiveness of digital twins as a key enabler for reliable, cost-efficient, and intelligent maintenance in next-generation smart manufacturing environments.