HOLISTIC PREDICTIVE MAINTENANCE FRAMEWORK LEVERAGING AI, VIBRATION ANALYSIS, AND THERMOGRAPHY FOR PROACTIVE ASSET RELIABILITY

The increasing complexity and scale of industrial operations demand maintenance paradigms that go beyondreactive and time-based strategies. As industries embrace digital transformation and strive for operationalexcellence, predictive maintenance has emerged as a strategic imperative to ensure asset reliability, minimizeunplanned downtimes, and optimize lifecycle costs. This study presents a holistic predictive maintenanceframework that synthesizes artificial intelligence (AI), vibration analysis, and infrared thermography to achieveproactive monitoring and early fault detection across diverse mechanical assets. Unlike conventional models thatrely on isolated condition indicators, the proposed framework integrates multi-sensor data to generate a compositehealth score for each asset in real time. Vibration analysis is employed to identify anomalies in rotating machinerythrough frequency spectrum shifts, while thermographic imaging captures surface temperature deviationsindicative of thermal stress, lubrication issues, or insulation breakdowns. These data streams are processed usingAI algorithms, including convolutional neural networks (CNNs) and random forest classifiers, which enableautomatic feature extraction, trend analysis, and fault classification. The system is further enhanced by edgecomputing modules for real-time inference and cloud-based dashboards for remote visualization. A caseapplication in a manufacturing plant demonstrates a 42% reduction in unscheduled downtime and a 28%improvement in maintenance planning efficiency. The findings validate that a synergistic approach to predictivemaintenance not only improves asset longevity and plant safety but also provides actionable insights forcontinuous process optimization. This paper offers a comprehensive roadmap for deploying AI-enhanceddiagnostic systems that align with Industry 4.0 objectives and sustainable reliability engineering.