## 5 Results
### Linear Wear-State Classification Approach  
The linear wear-state classification accuracy and MAE error results achieved using the STFT features extracted from the discrete-time signals are presented in Table 4. In the case of the SVM classification method, the lowest accuracy results were recorded at 27.5% for the one-third octave band feature set using a fine Gaussian kernel function. The highest classification performance achieved was 59.5% using the one-third octave band feature selection and a coarse Gaussian kerneling method.  
For the \(k\)-NN classification method, the lowest classification performance recorded was 39.9% for the linear band feature set using fine \(k\)-NN. The highest classification accuracy achieved was 54.2% by the one-third octave band features with cosine \(k\)-NN. The MAE results indicate that the coarse Gaussian kernel using the one-third octave band features was also the best-performing model with the lowest MAE score. The lowest error scores of 0.17 were achieved by both the cubic and cosine \(k\)-NN models.  
The results of the experiment using the signal envelope-derived features and linear wear-state classification are presented in Table 5. In the case of the SVM model, the lowest performance was recorded at 30.4% accuracy for the one-third octave band FFT features using a fine Gaussian SVM classifier. The highest classification performance, on the other hand, was achieved at 62.5%, for the one-third octave band summed envelope features using a Linear SVM kernel function.  
For the \(k\)-NN classification results, the lowest performance was recorded at 44.2% accuracy for the squared one-third octave band features from a fine \(k\)-NN classifier. The highest classification accuracy was achieved at 57.9% for the squared linear band features using a coarse \(k\)-NN classifier. The MAE results indicate that the linear, coarse and medium Gaussian kernel functions using the summed one-third octave band features were the best-performing model with the lowest MAE score. The coarse \(k\)-NN model proved to be the best option from the MAE values also, proving to be the most accurate in both Jaccard Index and MAE aspects.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{\(k\)-NN} \\ \hline
**Features** & **Fine** & **Medium** & **Coarse** & **Cosine** & **Cubic** & **Weighted** \\ \hline \(\mathcal{F}\Big{\{}x[n]\Big{\}}\) & 53.7 \(\,\)10.16 & 48.5 \(\,\)1.07 & 48.0 \(\,\)10.17 & 32.3 \(\,\)10.39 & 50.4 \(\,\)10.16 & 56.7 \(\,\)1.014 \\ \(L[m,k]\) & 48.7 \(\,\)10.18 & 50.6 \(\,\)1.07 & 43.7 \(\,\)10.22 & 33.1 \(\,\)10.36 & 48.2 \(\,\)10.17 & 55.1 \(\,\)1.014 \\ \(O[m,k]\) & 57.1 \(\,\)10.15 & 51.2 \(\,\)1.07 & 51.1 \(\,\)10.17 & 27.5 \(\,\)10.40 & 56.8 \(\,\)10.14 & **59.5 \(\,\)10.13** \\ \hline \multicolumn{7}{c}{\(k\)-NN} \\ \hline
**Features** & **Fine** & **Medium** & **Coarse** & **Cosine** & **Cubic** & **Weighted** \\ \hline \(\mathcal{F}\Big{\{}x[n]\Big{\}}\) & 41.5 \(\,\)10.23 & 43.8 \(\,\)1.02 & 44.7 \(\,\)10.19 & 45.1 \(\,\)10.20 & 43.7 \(\,\)10.21 & 43.0 \(\,\)10.22 \\ \(L[m,k]\) & 39.9 \(\,\)10.23 & 41.6 \(\,\)10.23 & 45.0 \(\,\)10.19 & 45.0 \(\,\)10.21 & 43.9 \(\,\)10.22 & 41.7 \(\,\)10.23 \\ \(O[m,k]\) & 52.2 \(\,\)10.19 & 51.5 \(\,\)10.18 & 53.4 \(\,\)10.17 & **54.2 \(\,\)10.17** & 50.7 \(\,\)10.19 & 51.8 \(\,\)10.18 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Linear wear-state classification results (%) and MAE (normalised) for STFT feature sets, where G denotes Gaussian.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**SVM**} \\ \hline
**Features** & **Linear** & **Quadratic** & **Cubic** & **Fine G** & **Medium G** & **Coarse G** \\ \hline \(\mathcal{F}\Big{\{}EA_{L}\Big{\}}\) & 47.9 \(\,\)10.20 & 48.5 \(\,\)10.18 & 42.0 \(\,\)10.22 & 36.9 \(\,\)10.32 & 51.9 \(\,\)10.17 & 51.2 \(\,\)1.016 \\ \(EA_{L}\) & 55.3 \(\,\)10.15 & 50.6 \(\,\)10.19 & 37.2 \(\,\)10.28 &