MACHINE LEARNING–DRIVEN PREDICTIVE MAINTENANCE IN RENEWABLE ENERGY FACILITIES: AN END-TO-END FRAMEWORK - Abdumalikov Akmaljon Abduxoliq ugli; Javokhir Sherbaev Ravshan ugli; Muhammad Eshonqulov Oktam ugli (SAMARALI TA’LIM VA BARQAROR INNOVATSIYALAR JURNALI, 2025)
Predictive maintenance (PdM) powered by machine learning (ML) has emerged as a vital tool in extending equipment life, reducing unplanned downtime, and lowering operational costs in distributed renewable energy systems—particularly wind and solar installations. This paper presents a comprehensive IMRaD-structured framework that integrates IoT data acquisition, preprocessing, ML models (anomaly detection, fault classification, RUL estimation), and scalable deployment strategies across edge/cloud environments. Using referenced case studies and results from existing literature, we demonstrate the efficacy of LSTM, Random Forest, and autoencoder models in achieving up to 95% accuracy for fault detection and reducing maintenance costs by ≈30–50%. We also discuss challenges such as data quality, interpretability, and cybersecurity, before exploring future directions including explainable AI (XAI), federated learning, and edge based digital-twin solutions.