Reinforcement learning-based bearing fault intelligent diagnosis of wind turbines

Wind turbine (WT) bearing health significantly impacts operational efficiency, but existing diagnosis methods struggle with noise and adaptability. This paper proposes a novel model to tackle these challenges. By framing the problem as a Markov decision process, the proposed approach automatically extracts features and learns optimal fault identification, eliminating manual effort. Tailored state/action spaces, reward functions, and exploration strategies enable the model to handle complexities in WT bearing signals effectively. The proposed intelligent diagnostic model was validated using the experimental data of WT bearing faults. Experiments demonstrate the superiority of the proposed approach over traditional methods, achieving significantly higher performance across diverse fault types. This paves the way for automated, intelligent, and universal WT bearing diagnosis, improving wind power reliability, safety, and cost-effectiveness. The study highlights the potential of the proposed method for tackling noisy, non-stationary data in complex industrial settings.