# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 5 Experimental Study
### Analysis of Experimental Results
#### 5.5.2 DCSF Experiments  
This group of experiments considers multiple source domains with different operating conditions from the target domain. However, all domains share the same fault types, reflecting common fault occurrences in practical scenarios. The target domain utilizes fault data from various operating conditions of other factories to aid in fault diagnosis. The experiments consist of four sets of tasks, each comprising 10 fault types. The target domain conditions are categorized as Load 3, Load 2, Load 1, and Load 0, respectively. For  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{Load 0} & \multicolumn{4}{c}{Load 1} \\ \hline  & WIOB & B & I & O & WIOB & B & I & O \\ \hline DS-MN & 0.93 & 0.88 & 0.92 & 0.93 & 0.96 & 0.86 & 0.98 & 0.81 \\ Finetune & 0.77 & 0.94 & 0.97 & 0.75 & 0.95 & 0.80 & 0.92 & 0.86 \\ FS-MN & 0.86 & 0.87 & 0.97 & 0.88 & 0.99 & 0.81 & 0.91 & 0.77 \\ FeatureKNN1 & 0.83 & 0.80 & 0.96 & 0.81 & 0.91 & 0.71 & 0.67 & 0.64 \\ FeatureKNN2 & 0.97 & 0.84 & 0.88 & 0.75 & 0.97 & 0.77 & 0.99 & 0.76 \\ FedCAE-MN & 0.91 & 0.84 & 1.00 & 0.80 & 0.99 & 0.80 & 0.93 & 0.84 \\ FedCDAE-MN & **0.94** & **0.89** & **1.00** & **0.94** & **0.99** & **0.86** & **1.00** & **0.93** \\ \hline  & \multicolumn{4}{c}{Load 2} & \multicolumn{4}{c}{Load 3} \\ \hline  & WIOB & B & I & O & WIOB & B & I & O \\ \hline DS-MN & 0.99 & 0.85 & 0.83 & 0.85 & 0.98 & 0.63 & 0.96 & 0.81 \\ Finetune & 1.00 & **0.85** & 0.77 & 0.85 & 0.78 & **0.95** & 0.99 & 0.90 \\ FS-MN & 1.00 & 0.72 & 0.80 & 0.80 & **1.00** & 0.86 & 0.96 & 0.85 \\ FeatureKNN1 & 0.99 & 0.62 & 0.78 & 0.71 & 0.98 & 0.79 & 0.79 & 0.73 \\ FeatureKNN2 & 1.00 & 0.82 & 0.84 & 0.94 & 0.99 & 0.61 & 0.92 & 0.77 \\ FedCAE-MN & 1.00 & 0.85 & 0.77 & 0.86 & 0.83 & 0.84 & **1.00** & 0.91 \\ FedCDAE-MN & **1.00** & 0.84 & **0.93** & **0.97** & 0.98 & 0.74 & 0.95 & **0.94** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average accuracy of the SCDF experiment.  
Figure 5: Overall accuracy of all methods.  
instance, when the target domain conditions are Load 3, the source domains are those with conditions of Load 0, Load 1, and Load 2 (denoted as Load 012).  
The experimental results are presented in Table 7. When the target domain experiences Load 3 conditions, the fault features learned from the source domain are relatively simple. Consequently, the generalization of Finetune, DS-MN, and FeatureKNN methods is poor, with an accuracy of less than 90%. In contrast, feature-space metric learning methods (FS-MN, FedCAE-MN, and FedCDAE-MN) exhibit outstanding performance, consistently achieving accuracies surpassing 95%. These results highlight the significance of leveraging label information from the source domain data to enhance feature extraction and the efficacy of few-shot metric learning strategies in improving generalization. Furthermore, the substantial improvement showcased by FeatureKNN2 in comparison to FeatureKNN1 highlights the effectiveness of convolutional denoising auto-encoders in elevating generalization within cross-domain scenarios. This improvement suggests that leveraging deep-learning techniques can significantly enhance the adaptability of fault-diagnosis methods to varying operating conditions, even when the source and target domains exhibit substantial differences. Notably, both FedCDAE-MN and FS-MN achieved an impressive average accuracy of 98%, indicating the efficacy of feature-space metric learning in addressing domain differences. In summary, our experiments indicate the pivotal role of load conditions in shaping fault feature complexity and its cascading impact on fault-diagnosis methods. It also reaffirms the value of leveraging source domain label information and the effectiveness of few-shot metric learning strategies in improving generalization.