This study presents a comprehensive end-to-end framework for Predictive Maintenance (PdM)
for components in a Micro grid, with a focus on Vertical Axis Wind Turbines and Energy Storage
Systems. The proposed framework integrates the development of tailored hardware for data
acquisition, predictive model development, and a Digital Twin (DT) that provides real-time data
visualization and future operating condition predictions. We integrate all components of the
PdM system, including the data collection hardware, predictive models, and the DT, into a
unified application using Microsoft Azure as the backend system. This comprehensive
framework serves as a scalable and robust solution for implementing PdM, providing a clear
pathway to reduce maintenance costs and improve system reliability in industrial settings. The
results indicate that this comprehensive framework can substantially reduce maintenance
costs while enhancing system resilience and sustainability, as demonstrated through a real-
world use case for wind turbines.