A parameter-adaptive spectral graph wavelet transform method for wind turbines vibration signal denoising

The signal noise can have a negative impact on the accuracy of engineering equipment condition assessment. However, the choice of decomposition layers and design parameters significantly impacts the outcomes of spectral graph wavelet transform (SGWT) methods. Conventional SGWT techniques often rely on predefined design parameters and decomposition layers, which can limit satisfactory decomposition results. To address this challenge, we present a novel parameter-adaptive SGWT method, referred to as PASGWT, designed for denoising vibration signals from wind turbines (WTs). PASGWT adaptively adjusts the optimal design parameters and decomposition layers to align with the signal characteristics. Furthermore, it introduces a novel evaluation metric to effectively identify fault-related features in signals corrupted by strong noise interference. Initially, the vibration signal is transformed into a directed graph representation. Subsequently, an SGWT with a warping function is constructed to process the signal. To guide the optimization process, we introduce a comprehensive evaluation index called the combination kurtosis index (CKI), which integrates periodic kurtosis and envelope spectrum kurtosis. The SGWT parameters are then optimized by the Hunter-Prey optimization (HPO) algorithm with maximum CKI value as the optimization objective. Finally, the denoised signal is reconstructed by selecting components with CKI values exceeding the average CKI value. The efficacy and practicality of the proposed method are validated through case studies involving simulated signals and two real-world fault signals from a scaled-down wind turbine test rig. Furthermore, comparative experiments highlight the superiority of the proposed method.