Traditional static filtering often fails to extract gear fault features under the variable speed conditions of wind turbines, while single-source signal analysis struggles against strong environmental noise. To address these limitations, this paper proposes a gear fault diagnosis framework integrating a synergistic closed-loop preprocessing mechanism with supervised multi-source information fusion. First, a “speed smoothing-dynamic filtering-envelope analysis” closed-loop mechanism is established to mitigate spectral ambiguity caused by non-stationary rotation. By dynamically adjusting band-pass filter parameters based on Gaussian-smoothed speed signals, this approach precisely locks onto fault feature frequency bands and significantly enhances the signal-to-noise ratio during envelope demodulation. Subsequently, a multidimensional feature system comprising time-domain statistics, frequency-domain energy, wavelet packet coefficients, and gear-specific meshing characteristics is constructed, utilizing Linear Discriminant Analysis (LDA) for supervised dimensionality reduction. Empirical analysis demonstrates that LDA utilizes fault label information more effectively than unsupervised alternatives like PCA or t-SNE, maximizing inter-class separability. Finally, an accuracy-weighted ensemble classifier is designed based on validation performance, integrating the decision-making strengths of SVM, KNN, and Random Forest models. Experimental validation on a high-fidelity wind turbine drivetrain test bench yields a diagnostic accuracy of 98.8% under complex variable speed conditions, outperforming existing single-source methods and conventional deep learning models while demonstrating superior robustness.
Kang et al. (Fri,) studied this question.