Fault diagnosis of bearings under variable speed and noise interference based on ED-GAFMD

To accurately identify bearing faults under variable speeds and strong noise, this paper proposes an improved Feature Mode Decomposition method (ED-GAFMD). Addressing the manual dependency and lack of adaptability in traditional FMD parameter selection (modes n, filter number K, and length L), this study utilizes the Grey Wolf Optimizer (GWO) with Permutation Entropy (PE) for automatic optimization. Signals are decomposed using optimal parameters, and sensitive modes are selected via the maximum kurtosis criterion for Envelope Order Analysis (EOA) to identify fault types. Validation using simulation and experimental data with mixed interference (harmonics, impulses, and white noise) demonstrates that ED-GAFMD outperforms GWO-FMD, GWO-VMD, and AFMD. It exhibits superior accuracy in extracting weak features and provides stable fault characterization. Furthermore, when applied to 1D-CNN diagnosis on signals with −2 to −10dB SNR, the method achieves an average accuracy of 96.52%, significantly enhancing diagnostic reliability under variable operating conditions compared to unfiltered signals.