###### Abstract  
Real-time bearing fault diagnosis necessitates highly accurate, computationally efficient, and energy-conserving models suitable for deployment on resource-constrained edge devices. To address these demanding requirements, we propose the Spike Convolutional Attention Network (SpikeCAN), a novel spike-driven neural architecture tailored explicitly for real-time industrial diagnostics. SpikeCAN utilizes the inherent sparsity and event-driven processing capabilities of spiking neural networks (SNNs), significantly minimizing both computational load and power consumption. The SpikeCAN integrates a multi-dilated receptive field (MDRF) block and a convolution-based spike attention module. The MDRF module effectively captures extensive temporal dependencies from signals across various scales. Simultaneously, the spike-based attention mechanism dynamically extracts spatial-temporal patterns, substantially improving diagnostic accuracy and reliability. We validate SpikeCAN on two public bearing fault datasets: the Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). The proposed model achieves 99.86% accuracy on the four-class CWRU dataset through five-fold cross-validation and 99.88% accuracy with a conventional 70:30 train-test random split. For the more challenging ten-class classification task on the same dataset, it achieves 97.80% accuracy under five-fold cross-validation. Furthermore, SpikeCAN attains a state-of-the-art accuracy of 96.31% on the fifteen-class MFPT dataset, surpassing existing benchmarks. These findings underscore a significant advancement in fault diagnosis technology, demonstrating the considerable practical potential of spike-driven neural networks in real-time, energy-efficient industrial diagnostic applications. capbtabboxtable[][  
account for over half of all mechanical system failures, leading to substantial economic losses due to unplanned downtime, costly repairs, and decreased productivity [3; 4]. Consequently, the development of accurate and efficient real-time fault detection and diagnosis methodologies is crucial for enhancing system reliability and operational safety. Traditionally, bearing fault diagnosis approaches typically involve manual extraction of discriminative features from vibration signals and classic machine learning algorithms [5]. While these techniques can be effective, they heavily rely on expert domain knowledge and are highly sensitive to noise and varying operating conditions. To overcome these limitations, researchers have increasingly adopted deep-learning-based methods, benefiting from their automated feature extraction capabilities and robust performance in noisy environments. In particular, convolutional neural networks (CNNs), renowned for their effectiveness in image processing tasks, have demonstrated significant success in bearing fault diagnosis applications [6; 7]. However, bearing vibration signals inherently possess temporal characteristics that CNN architectures alone may inadequately capture. This shortcoming has motivated further exploration into sequence-based models, such as recurrent neural networks (RNNs) [8; 9] and transformer architectures [10; 11], both of which have shown improved capability in modeling temporal dependencies. Despite the demonstrated performance improvements, deploying conventional deep learning architectures in practical, real-time industrial scenarios remains challenging. These deep networks usually feature a large number of parameters and complex computations, resulting in significant memory usage and computational costs [12; 13; 14; 15]. Such characteristics limit their suitability for real-time monitoring, especially in resource-constrained edge computing environments where power consumption and inference speed are critical considerations. Edge computing devices facilitate early fault detection and immediate response actions, significantly reducing the risk of minor faults escalating into severe breakdowns [16].