Bearing fault diagnosis based on efficient cross space multiscale CNN transformer parallelism

Fault diagnosis of wind turbine bearings is crucial for ensuring operational safety and reliability. However, traditional serial-structured deep learning models often fail to simultaneously extract spatio- temporal features from fault signals in noisy environments, leading to critical information loss. To address this limitation, this paper proposes a Wind Turbine Bearing Fault Diagnosis Model Based on Efficient Cross Space Multiscale CNN Transformer Parallelism (ECMCTP). The model first transforms one-dimensional vibration signals into two-dimensional time-frequency images using Continuous Wavelet Transform (CWT). Subsequently, parallel branches are employed to extract spatio-temporal features: the Convolutional Neural Network (CNN) branch integrates a multiscale feature extraction module, a Reversed Residual Structure (RRS), and an Efficient Multiscale Attention (EMA) mechanism to enhance local and global feature extraction capabilities; the Transformer branch combines Bidirectional Gated Recurrent Units (BiGRU) and Transformer to capture both local temporal dynamics and long-term dependencies. Finally, the features from both branches are concatenated along the channel dimension and classified using a softmax classifier. Experimental results on two publicly available bearing datasets demonstrate that the proposed model achieves 100% accuracy under noise-free conditions and maintains superior noise robustness under low signal-to-noise ratio (SNR) conditions, showcasing excellent robustness and generalization capabilities.