# Latent Dimensions of Auto-Encoder as Robust Features for Inter-Conditional Bearing Fault Diagnosis
## 6 Results
### Paderborn Dataset  
As the CMD was performing great along with our method during the previous comparison, and as the CNN was the best of source-only method, we will use these two for further comparisons on the Paderborn dataset. This dataset is known to be complicated because of the multitude of variations considered in their setup. That said, our method  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Transfer Task** & **SVM** & **CNN** & **CNN-MMD** & **MDDAN** & **MDIAN** & **CMD** & **MLCAE-KNN** \\ \hline C0 \(\rightarrow\) C1 & 70.70 & 72.25 & 81.00 & 87.15 & 99.60 & - & **100** \\ C0 \(\rightarrow\) C2 & 66.45 & 70.55 & 79.90 & 90.60 & 99.30 & 95.54 & **100** \\ C0 \(\rightarrow\) C3 & 63.40 & 62.45 & 55.85 & 91.65 & 99.10 & 99.54 & **100** (3\%) \\ C1 \(\rightarrow\) C0 & 71.30 & 87.30 & 88.95 & 84.00 & 99.70 & - & **100** \\ C1 \(\rightarrow\) C2 & 70.00 & 89.80 & 88.70 & 92.40 & 99.65 & - & **100** \\ C1 \(\rightarrow\) C3 & 74.00 & 74.70 & 80.50 & 94.20 & 99.80 & - & **100** (5\%) \\ C2 \(\rightarrow\) C0 & 62.85 & 60.35 & 64.65 & 87.40 & 97.60 & **100** & **99.8** \\ C2 \(\rightarrow\) C1 & 61.60 & 75.50 & 79.80 & 91.95 & 99.45 & - & **99.7** \\ C2 \(\rightarrow\) C3 & 67.65 & 84.30 & 79.95 & 91.50 & 99.45 & 96.9 & **100** (2\%) \\ C3 \(\rightarrow\) C0 & 65.30 & 66.90 & 75.25 & 84.25 & 97.45 & **100** & 99.9 (2\%) \\ C3 \(\rightarrow\) C1 & 65.70 & 81.15 & 71.15 & 87.35 & 98.60 & - & **99.9** (3\%) \\ C3 \(\rightarrow\) C2 & 63.25 & 74.95 & 74.85 & 92.15 & 99.50 & **100** & **100** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracies of various state-of-the-art methodologies on CWRU dataset. Except for SVM, CNN and MLCAE-KNN, the other methods are for domain adaptation. Also mentioned along with MLCAE-KNN is the accuracy variance across multiple iterations.  
shows promising results for various tasks, even on this dataset. The MLCAE-KNN clearly outperforms the CNN for every transfer task. From the results, it is also evident that our classifier is robust for some tasks such as P1-P3, P2-P3 and P3-P1 where even the CMD, the state-of-the-art domain adaptation method, was underperforming compared to ours.  
Further analyzing the results from this dataset, we notice that collecting data at higher scales and using our methodology for prediction provides better class transfer across conditions. For example, training on condition P0 and testing on conditions P1 and P3 have always provided better results than the other way around (From P1, P2 and P3 to P0). Here, P0's conditions are on a higher scale compared to the other three, as seen in Table 1, whereas, between other conditions such as P1, P2 and P3, class information transfer has been acceptable both ways. For this particular dataset, the CMD was better or as good as our method in the majority of the transfer tasks, as shown in the comparisons in Table 5. Overall, considering just the source domain data, the MLCAE-KNN has been robust in transferring the class information from one condition to another, proving its usability in an industrial context.