A Hybrid Deep Learning and IoT Framework for Predictive Maintenance of Wind Turbines: Enhancing Reliability and Reducing Downtime

The global shift towards renewable energy has positioned wind power as a cornerstone of sustainable development. However, the operational efficiency of wind farms is significantly hampered by unexpected component failures, leading to substantial downtime and maintenance costs. Traditional scheduled maintenance protocols are inefficient, often leading to unnecessary interventions or catastrophic failures. This study proposes a novel, robust framework for the predictive maintenance (PdM) of wind turbines, integrating Internet of Things (IoT) sensory data with a hybrid deep learning architecture. The proposed model leverages Convolutional Neural Networks (CNN) for feature extraction from vibrational and acoustic emission data, combined with Long Short-Term Memory (LSTM) networks to model the temporal dependencies inherent in time-series operational data. Drawing inspiration from successful applications of similar hybrid AI models in precision agriculture and smart farming, our approach is designed to accurately forecast the Remaining Useful Life (RUL) of critical components like gearboxes and bearings. We validate our framework on a benchmark dataset from NASA's Pronostia platform, demonstrating a 30% improvement in prediction accuracy over traditional single-model approaches and a 50% reduction in false alarms. The results underscore the potential of integrating hybrid AI and IoT, a paradigm successfully demonstrated in other complex systems, to create more reliable, efficient, and cost-effective maintenance strategies for the wind energy sector, thereby enhancing grid stability and accelerating the renewable energy transition.