To address the challenge of extracting effective fault features at low speeds, where fault information is weak and heavily influenced by environmental noise, a parameter-adaptive variational mode decomposition (VMD) method is proposed. This method aims to overcome the limitations of traditional VMD, which relies on manually set parameters. The sparrow search algorithm is used to calculate the fitness function based on mean envelope entropy, enabling the adaptive determination of the number of mode decompositions and the penalty factor in VMD. Afterward, the optimised parameters are used to enhance traditional VMD, enabling the decomposition of the raw signal to obtain intrinsic mode function components. The kurtosis criterion is then used to select relevant intrinsic mode functions for signal reconstruction. Finally, envelope analysis is applied to the reconstructed signal, and the results reveal the relationship between fault characteristic frequencies and their harmonics. The experimental results demonstrate that compared with other advanced methods, the proposed approach effectively reduces noise interference and extracts fault features for diagnosing low-speed bearing faults.

To address the issue of difficult extraction of bearing fault features caused by weak fault features and strong environmental noise in low-speed, a low-speed bearing fault diagnosis method based on wavelet threshold denoising and spectral amplitude modulation is proposed. The proposed method effectively overcomes the limitation that the traditional spectral amplitude modulation is greatly affected by noise in low-speed. Firstly, the raw signal is subjected to wavelet threshold denoising to reduce the interference of strong background noise, thereby obtaining the denoised signal. Secondly, the denoised signal is subjected to spectral amplitude modulation to enhance the bearing fault impulses. Finally, the envelope spectrum is normalized to facilitate the visual display of fault feature frequencies. The proposed method is analyzed through simulated and experimental signals in low-speed. The experimental results indicate that the proposed method can reduce noise interference and effectively extract fault features in low-speed.