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.
Water property forecasting can provide decision support for the protection and management of water resources. A big data analysis model, Multi-scale Extreme Learning (MEL), is reported in this work to address water property forecasting. Based on the divide-and-conquer philosophy, ensemble empirical mode decomposition is first adopted to decompose the Total Phosphorus (TP) that is a representation of water property into multi-scale features. The extreme learning machine is then employed to establish regression models in different scales. The outputs of multi-scale regression models are finally summarized into the ensemble forecasting result. A time series of historical weekly TP is introduced to validate the proposed MEL. Experimental results reveal that the proposed model based on the multiple scales representation capacity and the non-linear mapping, therefore, has the best excellent performance in water property forecasting.
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