JIN Chengfeng, PENG Yuhan, DUAN Xuetao, CHEN Guo, ZHENG Jianhu, ZHAO Huicheng, HU Zhongzhong, CAO Lingyan, SHEN Yaoyu, WAN Fu
The gearbox of a wind turbine is the core component for energy transmission in wind turbine units. Vibration signals, serving as a critical data source for fault diagnosis of the gearbox, can accurately reflect the internal dynamic characteristics of wind turbines. However, during actual operation, internal vibration signals of gearboxes are susceptible to multi-source compound noise interference, causing traditional signal denoising techniques to face bottlenecks in effectively separating noise. There is an urgent need to develop adaptive denoising methods for wind turbine gearboxes operating in complex service environments. This paper proposes a hybrid denoising method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), genetic algorithm (GA), and wavelet threshold denoising (WTD). By constructing three-dimensional time-frequency-energy evaluation metrics under unknown ground truth conditions and establishing a parameter space mapping model, the method achieves multivariate collaborative optimization of wavelet basis functions, decomposition levels, threshold rules, and correlation coefficient thresholds. The system overcomes the limitations of single metrics through complementary validation of smoothness, spectral entropy, and residual energy ratio. Experimental results demonstrate that this method elevates the signal-to-noise ratio (SNR) to 12.16 dB in simulated signals, and achieves a root mean square error of 0.78×10-2, significantly outperforming traditional algorithms in SNR and transient feature preservation. Through the hierarchical noise elimination mechanism combining CEEMDAN modal decomposition and WTD, along with GA’s global parameter optimization capability, it breaks through the bottlenecks of traditional methods that rely on empirical settings and struggle with coupled parameter optimization. This provides a high-fidelity signal preprocessing solution for early fault diagnosis of wind turbine gearboxes, holding significant engineering implications for enhancing the reliability of wind turbine condition monitoring systems.
