## 8 Discussion and Conclusions  
The experimental evaluations conducted on the MFPT and CWRU datasets clearly demonstrate the substantial potential and practical utility of the proposed SpikeCAN model for industrial fault diagnosis tasks. On the MFPT dataset, SpikeCAN achieved a remarkable accuracy of 96.31% across a challenging 15-class classification scenario, while maintaining an exceptionally low theoretical energy consumption of 0.78 mJ per inference. Similarly, on the widely used CWRU dataset, SpikeCAN delivered robust classification performance, achieving 97.80% accuracy in a constrained data scenario (10-class) and 99.86% accuracy in a 4-class configuration, consuming only 3.71 mJ per inference. Furthermore, inference speed tests conducted on an RTX 4090 GPU indicate a processing time of approximately 27.49 ms per inference, underscoring SpikeCAN's suitability for real-time monitoring tasks. Beyond these promising quantitative results, SpikeCAN also presents important practical advantages that enhance its suitability for real-world industrial deployments. The inherent energy efficiency of its fully spike-driven architecture directly facilitates its application on resource-constrained edge devices, potentially leading to substantial reductions in operational costs and overall energy demands. Additionally, extensive experimental evaluations and comprehensive ablation studies have validated the robustness of SpikeCAN, highlighting its reliable and consistent performance across diverse fault conditions. Additional techniques, such as adaptive spike thresholding and data augmentation, could be effectively integrated to enhance robustness in real-world environments characterized by sensor noise and operational variability. SpikeCAN's rapid inference capabilities, demonstrated empirically, also highlight its promise for timely fault detection and immediate corrective actions required in practical industrial monitoring scenarios. Nevertheless, we acknowledge several practical limitations that must be considered when interpreting these results. Primarily, the reported energy consumption values are theoretical estimates derived from model operations, and fully realizing SpikeCAN's \begin{table}
\begin{tabular}{l c c} \hline \hline
**Sample Count** & **Accuracy (\%)** & **F1 Score** \\ \hline
50 & 97.80 \(\pm\) 1.94 & 0.9674 \\
40 & 89.73 \(\pm\) 5.33 & 0.8827 \\
30 & 78.33 \(\pm\) 8.16 & 0.7555 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Robustness analysis for CWRU 10-class classification with varying sample counts (50, 40, and 30 per class). low-energy advantages hinges critically upon the continued development and broader availability of specialized neuromorphic hardware designed specifically for spike-driven computation. Achieving optimal real-world performance on such hardware platforms will require substantial hardware-specific optimization of SpikeCAN, including dedicated encoding strategies for efficiently converting raw sensor data into spike signals. Additionally, practical challenges, such as dealing with data scarcity for rare fault conditions and managing noise and variability in industrial environments, must be carefully addressed in future developments and deployments. Looking forward, several promising research opportunities emerge from this study. Extending SpikeCAN's architecture to handle various types of industrial time-series signals beyond vibration data, including acoustic, electrical, and multi-modal sensor inputs, represents a compelling direction. Additionally, examining the interpretability of attention patterns learned by SpikeCAN, especially regarding their alignment with fault-relevant signal features, could provide valuable insights for enhanced diagnostic transparency and trustworthiness. Furthermore, integrating unsupervised or semi-supervised learning techniques could enable SpikeCAN to dynamically adapt to evolving operational conditions or previously unseen fault types without extensive labeled datasets.