Cross-domain fault identification of bearings based on synchrosqueezed wavelet–scattering transform and bagging–convolutional neural network

Cross-domain fault diagnosis of rolling bearings plays a pivotal role in maintaining mechanical system reliability and operational safety, particularly under complex operational environments where bearing failures frequently occur. To improve the accuracy of cross-domain fault identification, a novel cascaded synchrosqueezed wavelet–scattering transform and bagging–convolutional neural network (SWST-BCNN) was proposed in this paper. Firstly, a synchrosqueezed wavelet–scattering transform stage was designed. It preserves discriminative fault attributes in synchrosqueezed wavelet-based time–frequency representations while compressing feature dimensionality. Then, a bagging–convolutional neural network stage was designed to improve accuracy by integrating several different CNN classifiers. Finally, the proposed SWST-BCNN network was verified on the public bearing dataset of Case Western Reserve University and was compared with some classical algorithms. The results showed that the proposed SWST-BCNN network exhibited higher accuracy. This paper provides a feasible method for cross-domain fault identification of bearings.