## 5 Result
### Results on CWRU Dataset  
For the four-class classification task on the CWRU dataset, we evaluated the performance of our SpikeCAN using two validation approaches: five-fold cross-validation and a standard train/test split. The proposed SpikeCAN demonstrated exceptional performance and stability in a five-fold cross-validation on the CWRU dataset. As shown in Table 2, SpikeCAN achieved a mean accuracy of 99.86% and a mean F1 Score of 0.9982, using only 1.45 million parameters and consuming merely 3.71 mJ per inference. Compared to SEW-Resnet18, which obtained a mean accuracy of 87.46% and a mean F1 Score of 0.8400, SpikeCAN showed statistically significant improvement in terms of both accuracy (t-statistic = 3.7816, \(p\) = 0.0052) and F1 Score (t-statistic = 4.5924, \(p\) = 0.0016). Furthermore, SEW-Resnet18 exhibited considerably higher performance variance across folds, indicating lower stability relative to SpikeCAN. When compared to SpikeVGG(A), SpikeCAN exhibited marginally higher accuracy and F1 Score. While these differences were not statistically significant (accuracy: t-statistic = 0.8730, \(p\) = 0.4077; F1 Score: t-statistic = 1.0498, \(p\) = 0.3230), the critical differentiating factor is energy efficiency. In terms of energy consumption, SpikeCAN demonstrated approximately 32.3 times higher efficiency compared to its ANN counterpart, which consumed 119.68 mJ per inference. Moreover, compared to the conventional ANN-based VGG(A) model consuming 239.67 mJ per inference, SpikeCAN exhibited approximately 64.6 times superior efficiency. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **F1 Score** & **Param** & **Energy (mJ)** \\ \hline Cluster KD [37] & 97.59 & 0.9961 & - & - \\ ResNet 18 [38] & 93.94 \(\pm\) 0.85 & 0.9247 & 3.86 M & 51.84 \\ SEW-Resnet [26] & 84.27 \(\pm\) 1.63 & 0.8079 & 3.86 M & 5.56 \\ SpikeCAN (Ours) & 96.31 \(\pm\) 0.76 & 0.9560 & 1.46 M & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison in MFPT. Figure 3: Confusion matrices illustrating the classification performance on MFPT. Even when compared to the spike-based variant, SpikeVGG(A), which consumed 16.94 mJ per inference, SpikeCAN demonstrated approximately 4.6 times greater energy efficiency. Therefore, SpikeCAN not only delivers superior and more consistent classification accuracy compared to SEW-Resnet18 but also presents a more compelling alternative to SpikeVGG(A), achieving comparable top-tier classification performance with substantially improved energy efficiency. Furthermore, to provide comparisons with conventional algorithms, we conducted additional experiments using a 70:30 random split for training and testing datasets. To ensure robust evaluation, this random splitting procedure was repeated five times with distinct random seeds. As detailed in Table 3, SpikeCAN achieved an average classification accuracy of 99.88% under these conditions. Corresponding confusion matrices for each random seed are presented in Figure 4, clearly illustrating the consistency and reliability of SpikeCAN's classification performance. Unlike traditional bearing fault diagnosis approaches [34; 35; 40; 41; 42; 43; 44], which typically rely on handcrafted feature extraction methods prior to classification, SpikeCAN operates in a fully end-to-end training using raw data without any additional feature extraction. It eliminates the need for separate signal processing or manual feature engineering, thereby reducing computational overhead while maintaining, or even exceeding, the diagnostic accuracy deployment in real-time industrial monitoring applications, particularly in resource-constrained environments.