A study on bearing fault diagnosis based on ACYCBD-MTF-MobileViT under strong noise

Aiming at the problems of poor noise robustness and insufficient model training of traditional deep learning models in small-sample and strong noise environment, a method based on adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD) combined with Markov transition field (MTF) and MobileViT was proposed for rolling bearing fault diagnosis. Firstly, the impact signal of bearing faults under strong noise background was enhanced by a parameter-adaptive CYCBD algorithm to reduce the influence of strong background noise, then, MTF was used to transform the preprocessed one-dimensional bearing vibration signal into a two-dimensional feature image with temporal correlation, and finally, the MTF image was input into the Mobile ViT network for training to get fault diagnosis results, which is applied to the Southeast University Gearbox Dataset and Shenyang University of Technology Laboratory rolling bearing dataset to verify the fault identification accuracy of the proposed method in small sample strong noise conditions. Results show that, in the small sample strong noise conditions, for ACYCBD preprocessed data, the trained model has a higher accuracy. Compared to the data preprocessed by maximum correlated kurtosis deconvolution, variational mode decomposition, ensemble empirical mode decomposition, the accuracy increased by 1.73%, 1.99%, and 2.2%, respectively. After using MTF for modal transformation, the accuracy is 2.59%, 3.12%, and 2.72% higher than that of Gramian angular field, continuous wavelet transform and RP, respectively. Compared with other deep learning models, the method proposed in this paper has higher anti-interference ability and generalization performance under the above conditions. © 2024 Chinese Vibration Engineering Society. All rights reserved.