Spectral trend-guided empirical wavelet transform and its application in wheelset-bearing fault diagnosis
Wheelset-bearing is a critical component for ensuring the safe and stable operation of high-speed trains, and its fault diagnosis is of great importance. Empirical wavelet transform (EWT) offers an adaptive decomposition framework for wheelset-bearing fault diagnosis, but its performance heavily depends on the selection of spectral segmentation boundaries. To address this limitation, this paper proposes a novel adaptive decomposition method, named spectral trend-guided empirical wavelet transform (STGEWT), which integrates the proposed spectral domain Fourier transform-based spectral trend extraction (SDFTSTE) and the multi-band refined ratio of cyclic content (MRRCC) metric. The SDFTSTE captures the low-frequency content of the spectral signal by removing its high-frequency components, thereby yielding a smooth spectral trend curve that serves as the basis for determining the segmentation boundaries. The MRRCC selects the fault-related components by integrating amplitude information over multiple harmonic bands of fault characteristic frequencies. The effectiveness of the proposed STGEWT is validated using both simulated and experimental vibration signals. Comparative analyses with other methods demonstrate that STGEWT consistently achieves superior performance. The results indicate that STGEWT can be regarded as an effective and reliable tool for wheelset bearing fault diagnosis under complex operating conditions.