## 2 Related Work
### SNNs for Industrial Fault Diagnosis  
Bearing fault diagnosis tasks inherently require continuous, real-time monitoring to detect faults and initiate immediate corrective actions promptly. Such monitoring typically occurs directly on edge devices, which must operate effectively under significant constraints in terms of computational resources, energy consumption, and available bandwidth. Processing data locally at the edge not only minimizes delays in fault detection but also reduces communication overhead with centralized servers, enabling autonomous operation in remote or inaccessible industrial settings. Under these demanding conditions, SNNs, characterized by their event-driven computation and significantly low energy consumption due to sparse spike-based processing, emerge as highly promising solutions for bearing fault diagnosis. The spike-driven nature of SNNs substantially reduces their computational load and energy requirements, making them exceptionally suitable for real-time monitoring on resource-constrained edge devices. Additionally, SNNs possess the intrinsic capability to effectively handle diverse input signals, dynamically modulating their internal membrane potentials in response, which can enhance their learning capabilities and diagnostic performance [33]. Bearing fault signals inherently contain a wide spectrum of distinct patterns corresponding to various operational and fault states, aligning effectively with this advantageous characteristic of SNNs. To date, only a few studies [34; 35] have explicitly explored the application of SNNs for bearing fault diagnosis. Both start from an efficient constraint of how to turn continuous vibration magnitudes into spikes. The earlier study [34] employed Gaussian Receptive Field(GRF) population coding, where each measurement is translated into spike timings using multiple Gaussian functions. The resulting spikes are classified using a simple network composed of Pulse-Stream Rate Model (PSRM) neurons using a shallow, fully connected multi-layer network. The PSRM structure was selected mainly due to its simplicity and ease of use with limited training data. The follow-up study [35] further improved the GRF encoding method and added another hidden layer. Both models heavily rely on handcrafted feature extraction techniques, local mean decomposition (LMD), which decomposes vibration signals into amplitude- and frequency-modulated components, such as skewness, kurtosis, and crest factor. Although LMD effectively extracts bearing-related modes, these multi-stage pipelines introduce considerable computational complexity, require extensive domain expertise, and incur increased energy consumption due to non-spiking operation. The reliance on non-spike-driven preprocessing diminishes the fundamental energy efficiency advantages of SNNs, limiting the feasibility of end-to-end spike-driven processing for deployment in real-time, energy-constrained edge computing environments. Moreover, because these approaches rely on shallow networks with limited capacity, they struggle to capture complex temporal patterns present in bearing faults. In contrast, our proposed SpikeCAN model directly processes raw vibration signals in a fully spike-driven manner, obviating the need for separate feature extraction stages. SpikeCAN is also built upon a deep SNN framework integrated with spike-compatible attention mechanisms, enabling autonomous learning of salient temporal and spatial features. Attention mechanisms have become essential for enhancing interpretability and performance in deep neural networks. The convolutional block attention module (CBAM) [36] efficiently derives channel and spatial attention maps through sequential pooling and convolution operations, proving effective primarily within convolutional architectures tailored for spatial data. Nevertheless, the spatially oriented design of CBAM limits its direct applicability to time-series datasets.