###### Abstract
Additionally, processing data locally on edge devices reduces the communication burden on centralized systems, supports autonomous operation in remote or inaccessible installations, and enhances overall system efficiency through reduced energy consumption. These advantages become increasingly important for real-time monitoring in large-scale or remotely situated industrial systems. In response to these challenges, spiking neural networks (SNNs) [17] have emerged as a compelling alternative. SNNs process information using discrete binary spike signals and operate based on an event-driven computing paradigm, substantially reducing energy consumption compared to traditional neural networks. Specifically, the SNN architecture replaces computationally intensive multiply-and-accumulate (MAC) operations with simpler accumulate (AC) operations, exploiting their inherent sparsity and asynchronous processing nature [18]. Their asynchronous, event-driven nature keeps the network largely inactive until meaningful input spikes arrive, thereby further lowering power requirements. This advantage is especially suitable for real-time industrial monitoring scenarios, where memory and computational resources are limited but inference speed is critical. Nevertheless, spike-driven SNNs commonly exhibit lower performance accuracy compared to conventional deep neural networks, primarily due to the sparsity and limited information representation of spike signals [19]. To mitigate this performance degradation, hybrid neural network architectures combining SNNs and artificial neural networks (ANNs) have been proposed [20]. These approaches typically integrate SNNs with ANN-based modules to enhance accuracy. However, they inevitably reintroduce MAC operations, undermining the computational and energy-efficiency benefits of purely spike-driven systems. As a result, research has increasingly shifted toward improving performance within fully spike-driven architectures that preserve the energy efficiency of SNNs. A key challenge in this pursuit is converting continuous-valued input signals into spike trains. Among the various encoding techniques explored--such as rate coding [21] and temporal coding [22]--direct encoding has shown notable advantages. By mapping continuous values directly into spike magnitudes at each timestep, direct encoding effectively retains informative content with fewer timesteps, thereby reducing latency and memory requirements [23; 24]. In parallel, the inherent non-differentiability of spike signals has been addressed using surrogate gradient learning methods, which approximate gradients and enable efficient backpropagation training in spike-based networks [25]. Building upon these foundational advancements, attention-based SNN models--such as SEW-ResNet--have emerged, demonstrating that spike-driven architectures can achieve performance comparable to ANN counterparts while maintaining significantly lower energy consumption [18; 26; 27; 28]. These innovations have made it possible to build SNN models that perform well while still using much less energy and computing power. Despite recent advances in SNNs, existing approaches for bearing fault diagnosis have yet to simultaneously achieve ANN-level accuracy and genuine event-driven efficiency. Furthermore, previous SNN methods have not effectively incorporated attention mechanisms to dynamically weight spatial-temporal features and adequately extend receptive fields, which is crucial for processing long-sequence vibration signals commonly encountered in bearing fault diagnosis. Motivated by these developments, we propose the spike convolutional attention network (SpikeCAN), a fully spike-driven architecture explicitly tailored for energy-efficient, real-time bearing fault diagnosis. SpikeCAN incorporates two novel modules: the spike attention module and the multi-dilated receptive field (MDRF) convolutional module.