# Novel Methodology for Condition Monitoring of
## 6 Comparative Methodology  
In order to highlight the contribution of the proposed condition monitoring based on the analysis of acquired infrared images, the obtained results have been compared with classical condition monitoring approaches that propose the estimation and evaluation of a high-dimensional set of features and that also propose the analysis of vibration signals. Therefore, regarding the consideration of a high-dimensional set of features, the set of thermal statistical features estimated from the thermal matrices is directly evaluated under the same structure of the ANN classifier proposed in this work. As a result, Tables 5 and 6 summarize the confusion matrices estimated by the proposed ANN structure when the original set of statistical thermal features, estimated from the thermal matrices, is directly assessed. From these obtained results, it should be highlighted that the exclusion of the dimensionality reduction stage, performed by LDA, significantly affects the classification performance, and although all the data variance of the original set of thermal features is evaluated by the ANN structure, a more complex structure of ANN classifier must be required to perform accurate results. Subsequently, during the training and evaluation of the original set of statistical thermal features, classification ratios of about 86.2% and 60% were achieved, respectively. Therefore, it is demonstrated that including a dimensionality reduction stage, by means of LDA, allows for an improved characterization of the considered conditions due to only the most significative and useful information being retained; additionally, the classification task is facilitated, resulting in the consideration of a classical structure for the proposed ANN structure. Indeed, when the original set of features estimated from thermal matrices is directly evaluated with the same structure of the proposed ANN classifier, a low classification performance is obtained due to the original set of features requiring the consideration of a complex structure of ANN classifier for modeling all the considered conditions. On the other hand, the results of the proposed condition monitoring methodology are also compared with classical monitoring approaches that consider the analysis of vibration signals; therefore, when the considered conditions were experimentally evaluated, at the same time that the infrared images were captured, mechanical vibration signals were also continuously acquired by means of a triaxial accelerometer model LIS3L02 AS4. This accelerometer was installed on the top of the gearbox to perform continuous vibration acquisition in the perpendicular plane of the axis of rotation in the gearbox. The vibration signals were acquired at a sampling frequency of 3 kHz during 90 s of the continuous working condition of the gearbox transmission system. Once the vibration signals had been obtained, the proposed condition monitoring methodology was applied by taking into account the acquired vibration signals instead of the thermal images; \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Target Class**} \\ \cline{3-6}  & & **HTL** & **25\%** & **50\%** & **75\%** \\ \hline \multirow{3}{*}{**Output Class**} & **HTL** & 6 & 0 & 4 & 8 \\  & **25\%** & 4 & 10 & 0 & 0 \\ \cline{1-1}  & **50\%** & 0 & 0 & 6 & 0 \\ \cline{1-1}  & **75\%** & 0 & 0 & 0 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for the validation of the ANN classifier with extraction of statistical features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Target Class**} \\ \cline{3-6}  & & **HTL** & **25\%** & **50\%** & **75\%** \\ \hline \multirow{3}{*}{**Output Class**} & **HTL** & 33 & 0 & 10 & 0 \\  & **25\%** & 0 & 39 & 1 & 0 \\ \cline{1-1}  & **50\%** & 7 & 0 & 28 & 2 \\ \cline{1-1}  & **75\%** & 0 & 1 & 1 & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix for the training of the ANN classifier with extraction of statistical features.