Rolling Bearing Fault Diagnosis Based on CKEPM Encoding and Tri-CrossViT

Rolling bearings are indispensable components in mechanical equipment, and their condition directly affects the operational reliability and maintenance costs. To address the requirements of high-precision and robust bearing fault diagnosis, this paper proposes a dynamic-mapping method named CKEPM, based on Complete Ensemble Empirical Mode Decomposition (CEEMDAN), kurtosis-, energy-, and Pearson-correlation-based intrinsic mode function (IMF) selection, followed by Markov Transition Field (MTF) encoding. Furthermore, a triple-scale, cross-branch network Tri-CrossViT is constructed to hierarchically model fault-related features at different resolutions and enable cross-scale information interaction for bearing fault classification. Specifically, vibration signals are first decomposed using CEEMDAN and the fault-sensitive intrinsic mode functions are selected based on kurtosis, energy, and Pearson correlation criteria. Then, these components are encoded by the Markov Transition Field to form RGB time-frequency images. Subsequently, the proposed Tri-CrossViT processes the generated images through three parallel branches with different patch resolutions to capture fault-related information at triple scales, including localized impulsive transients, modulation-related envelope structures, and global spectral energy distributions, and a Cross-branch attention is introduced to fuse complementary fault information across scales, enabling joint learning of local impulses and global dynamic characteristics. The Tri-CrossViT achieves diagnostic accuracies of 98.87% and 99.51% on the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets, respectively, realizing outstanding classification performance. The experimental results demonstrate the effectiveness of the integrated CKEPM-Tri-CrossViT framework for rolling bearing fault diagnosis under the evaluated benchmark conditions.