## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**SVM**} \\ \hline
**Features** & **Linear** & **Quadratic** & **Cubic** & **Fine G** & **Medium G** & **Coarse G** \\ \hline \(\mathcal{F}\Big{\{}EA_{L}\Big{\}}\) & 63.7 \(\mid\) 0.13 & 63.5 \(\mid\) 0.17 & 60.4 \(\mid\) 0.19 & 63.9 \(\mid\) 0.15 & 67.8 \(\mid\) 0.12 & 63.3 \(\mid\) 0.12 \\ \(EA_{L}\) & 65.9 \(\mid\) 0.11 & 64.8 \(\mid\) 0.16 & 57.8 \(\mid\) 0.19 & 66.0 \(\mid\) 0.16 & 72.0 \(\mid\) 0.09 & 68.5 \(\mid\) 0.11 \\ \(EA_{L}^{A}\) & 62.7 \(\mid\) 0.13 & 14.7 \(\mid\) 0.68 & 27.7 \(\mid\) 0.38 & 66.9 \(\mid\) 0.13 & 69.1 \(\mid\) 0.11 & 68.1 \(\mid\) 0.12 \\ \(\mathcal{F}\Big{\{}EA_{O}\Big{\}}\) & 71.2 \(\mid\) 0.20 & 65.5 \(\mid\) 0.30 & 67.5 \(\mid\) 0.31 & 65.2 \(\mid\) 0.27 & 73.0 \(\mid\) 0.21 & 68.2 \(\mid\) 0.21 \\ \(EA_{O}\) & **73.1 \(\mid\) 0.09** & 68.1 \(\mid\) 0.13 & 56.7 \(\mid\) 0.23 & 60.4 \(\mid\) 0.18 & 66.3 \(\mid\) 0.11 & 68.9 \(\mid\) 0.12 \\ \(EA_{O}^{A}\) & 66.7 \(\mid\) 0.11 & 36.2 \(\mid\) 0.41 & 9.5 \(\mid\) 0.48 & 60.9 \(\mid\) 0.15 & 70.2 \(\mid\) 0.11 & 70.3 \(\mid\) 0.11 \\ \hline \hline \multicolumn{6}{c}{**k-NN**} \\ \hline
**Features** & **Fine** & **Medium** & **Coarse** & **Cosine** & **Cubic** & **Weighted** \\ \hline \(\mathcal{F}\Big{\{}EA_{L}\Big{\}}\) & 67.5 \(\mid\) 0.15 & 65.2 \(\mid\) 0.15 & 68.3 \(\mid\) 0.13 & 64.1 \(\mid\) 0.16 & 65.4 \(\mid\) 0.15 & 65.0 \(\mid\) 0.15 \\ \(EA_{L}\) & 69.8 \(\mid\) 0.14 & 71.6 \(\mid\) 0.13 & 72.5 \(\mid\) 0.10 & 70.7 \(\mid\) 0.13 & 71.8 \(\mid\) 0.13 & 71.7 \(\mid\) 0.13 \\ \(EA_{L}^{A}\) & 73.1 \(\mid\) 0.12 & 73.0 \(\mid\) 0.11 & 73.5 \(\mid\) 0.10 & 72.4 \(\mid\) 0.10 & 73.2 \(\mid\) 0.11 & 72.8 \(\mid\) 0.11 \\ \(\mathcal{F}\Big{\{}EA_{O}\Big{\}}\) & 74.1 \(\mid\) 0.09 & 73.0 \(\mid\) 0.08 & 72.0 \(\mid\) 0.08 & **74.3 \(\mid\) 0.08** & 72.0 \(\mid\) 0.08 & 73.3 \(\mid\) 0.08 \\ \(EA_{O}\) & 66.0 \(\mid\) 0.15 & 64.4 \(\mid\) 0.13 & 67.0 \(\mid\) 0.12 & 63.0 \(\mid\) 0.14 & 63.7 \(\mid\) 0.13 & 64.4 \(\mid\) 0.13 \\ \(EA_{O}^{A}\) & 62.0 \(\mid\) 0.16 & 63.3 \(\mid\) 0.14 & 63.1 \(\mid\) 0.13 & 66.0 \(\mid\) 0.15 & 63.1 \(\mid\) 0.15 & 63.0 \(\mid\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Non-linear wear-state classification results (%) and normalised MAE for envelope analysis features, where G denotes Gaussian. All of the ML recipes presented performed very well on class 1, the max and min range being 92.6% to 89.0% in comparison to the max and min range for class 5 of 68.1% to 41.6%. The ML recipe with the best performance overall at 62.5% with MAE of 0.12 for the linear wear-state classification was that of the \(EA_{O}\) features with a SVM (Linear) algorithm, this is shown in matrix (c) of Figure 7. This particular ML recipe achieved the highest classification for class 4 at 65.1% and was second best in class 1, 3 and 5, which ultimately led to it achieving the best overall score. Similarly, as shown in Figure 8, the classification performance for class 1  
Prior work by [PERSON] et al. [78], [PERSON] et al. [79] a nd [PERSON] et al. [80], presented ML methods that achieved percentage accuracy scores of 76.2%, 67.20% and 77.44%, respectively, using the PRONOSTIA bearing dataset. However, these proposed methods utilised a framework where only bearings S. 01 and S. 02 were used for training the algorithm, and the remaining five bearings were used for testing. The round-robin experimental framework presented in this paper presents the mean percentage accuracy of all seven bearing signals whereas the prior work only presented the mean of five signals. Furthermore,  
Figure 8: Confusion matrices of the best-performing ML recipes for the non-linear wear state classes: (a) \(O[m,k]\) with SVM (Medium G), (b) \(O[m,k]\) with \(k\)-NN (Coarse), (c) \(EA_{O}\) with SVM (Linear) and (d) \(\mathcal{F}\big{\{}EA_{O}\big{\}}\) with \(k\)-NN (Cosine).