AI-Enabled Predictive Analytics for Proactive Maintenance in IoT Systems

Wind turbine operations and maintenance expenditures have always been a significant burden, with the majority of money spent on unforeseen unorganized failures, fixes, and downtime expenses. Commercial Internet of Things (IoTs) and artificial intelligence (AI) technological advances have achieved breakthroughs and are truly revolutionary innovations with the ability to monitor, forecast, and avoid catastrophic breakdowns. A Sensor-oriented IoT system (SOIS) will aid in the monitoring of critical characteristics of wind turbines that govern their operating circumstances, such as wind pace, motion, temperature, and power generated. For proactive maintenance of wind turbines, an integrated strategy using Machine Learning (ML) approaches for SOIS was applied. The system's diagnostic component receives sensor data and does predictive analytics in cloud environment. Five ML techniques are used, and the outcomes are summarized for prediction. The techniques' outputs are contrasted for the accuracy of the detected statistics, and it is discovered that the Optimized Artificial Neural Network performs better for proactive maintenance and predictive analytics of the power of wind turbines.