Improving Static And Dynamic Load Capacities Of Hybrid Ceramic Ball Bearings In Wind Generators With Artificial Intelligence-Driven Design Strategies

This research introduces a novel AI-driven optimization technique to enhance hybrid ceramic ball bearings in wind turbine generators. It focuses on improving both static and dynamic load capacities to address the challenges posed by harsh operating conditions. By utilizing a unique dynamic approach for assigning probabilities in the crossover and mutation processes of a genetic algorithm, significant improvements were achieved. The study recorded an 11.90% increase in dynamic load capacity and an 11.25% boost in static load capacity. These enhancements are critical for reducing failure risks and ensuring the reliability of bearings under high-stress conditions. Wind turbines operate in demanding environments, making durable and efficient components essential for long-term functionality. Improved bearing performance contributes directly to the robustness and reliability of wind energy systems. This advancement not only strengthens bearing technology but also supports more efficient wind power generation. Enhancing the durability of wind turbines can lead to more cost-effective and sustainable renewable energy solutions. Ultimately, this research sets new standards for both bearing design and renewable energy optimization.