Spectral-Clustering-Guided Fourier Decomposition Method and Bearing Fault Feature Extraction

The Fourier decomposition technique has notable advantages in filtering vibration acceleration signals and enhances the feasibility of frequency-domain mode decomposition. To improve the accuracy of mode extraction, this paper proposed a novel Fourier decomposition technique based on spectral clustering. The methodology comprises three key steps. First, spectral clustering is performed using feature vectors derived from the spectrum envelope, specifically the frequency and amplitude of its maximum value, along with the average amplitude of local spectral peaks. Subsequently, the spectrum is adaptively segmented based on clustering feedback to determine spectral segmentation boundaries. Followed by this, a filter bank is constructed via Fourier decomposition for signal reconstruction. Finally, a harmonic correlation index is computed for all decomposed components to identify fault-sensitive modes exhibiting the highest diagnostic relevance. These selected modes are subsequently subjected to demodulation for fault diagnosis. The effectiveness of the proposed method is validated through both simulated signals and experimental datasets, demonstrating its improved ability to capture critical fault information.