Adaptive Weighted Feature Mode Decomposition and Its Application in Bearing Fault Diagnosis

A Rolling Bearing Fault Diagnosis Method Using Adaptive Decomposition and Impact Feature Enhancement Fusion

A novel fault diagnosis method based on adaptive signal decomposition and intelligent classification integration is proposed, outperforming other comparative models and exhibiting superior diagnostic precision and robustness.

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 selectio...

Fast multivariate all time-scale decomposition method and its application in gear fault diagnosis

Aiming at the limitations of existing multivariate signal decomposition methods such as fast multivariate empirical mode decomposition (FMEMD) and completely adaptive projection multivariate local characteristic-scale decomposition (CAPMLCD) for gear fault diagnosis, this paper proposes a fast multivariate all-time-sca...

A Novel Narrowband Filtering Demodulation Method Based on Adaptive Multi-Level Spectra Segmentation Strategy and Its Application in Bearing Fault Diagnosis

Rolling bearings, as a key component of rotating machinery, require precise fault diagnosis to ensure the safe and reliable operation of industrial systems. Nevertheless, the performance of traditional narrowband filtering demodulation (NFD) methods is constrained by inherent limitations of spectral segmentation framew...

Adaptive smoothed soft-threshold method for noise-robust bearing fault diagnosis

A robust multi-class bearing fault diagnosis framework using envelope analysis, cepstrum prewhitening and machine learning

The proposed framework provides an effective balance between diagnostic accuracy, robustness, interpretability, and computational efficiency, making it a promising solution for intelligent condition monitoring and predictive maintenance of rotating machinery.