# Novel Methodology for Condition Monitoring of
## 5 Results
Moreover, it should be mentioned that this resulting 2D projection results from a linear combination containing different weights from the original features, where the most significative features have a greater weighting value. After obtaining the data set reduced into 2D space with the LDA strategy, the automatic fault classification is carried out through the ANN-based algorithm. In this sense, due to the consideration of the feature reduction stage by means of LDA, which allows the obtainment of a visual representation of all considered conditions in a 2D space and facilitates the classification task, the structure of the considered ANN classifier is based on a classical structure composed only of the input layer, a single  
Figure 5: Acquired signals for the condition monitoring of gears in a gearbox transmission system with infrared thermography: (**a**) HTL gears, and (**b**) 25%, (**c**) 50%, and (**d**) 75% uniform wear in the gears. Figure 6: Projection of features, reduced by LDA, that characterize the condition of the gearbox onto the data set of infrared thermography. hidden layer, and the output layer. The input layer consists of two neurons, the hidden layer has ten neurons as the classical implementation of ANN classifiers suggests, and the output layer is composed of four neurons. Subsequently, in order to obtain statistically significant results, the ANN-based classifier is trained and validated under a five-fold cross-validation scheme. In this regard, taking into consideration all the conditions evaluated (HTL, and 25%, 50% and 75% uniform wear of gears), the original database that consists of 200 samples, 50 samples per condition, is divided into two different data sets for training and validation purposes. Therefore, the first data set used for training is composed of 160 samples, 40 samples per condition, while the data set used for validation consists of 40 samples, 10 samples per condition. To analyze the performance of the classification, a five-fold cross-validation scheme is applied to determine the variability of the training and the validation data of the classifier. Therefore, four types of classification are averaged, obtaining 100% in the classification index for both training and validation. Tables 3 and 4 summarize the confusion matrices obtained during the training and validation of the proposed ANN classifier. As it can be noted in these Tables, the global classification ratio achieved during the training and validation is about 100%. Moreover, the consideration of the ANN as a classifier also allows the calculation of the classification regions, which provides a visual representation of the region that belongs to each evaluated condition. In this sense, Figure 7a,b shows the classification regions obtained by the ANN classifier during the training and validation procedures, respectively. \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** & 10 & 0 & 0 & 0 \\  & **25\%** & 0 & 10 & 0 & 0 \\ \cline{1-1}  & **50\%** & 0 & 0 & 10 & 0 \\ \cline{1-1}  & **75\%** & 0 & 0 & 0 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix for the validation of the ANN classifier with infrared imaging. \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** & 40 & 0 & 0 & 0 \\  & **25\%** & 0 & 40 & 0 & 0 \\ \cline{1-1}  & **50\%** & 0 & 0 & 40 & 0 \\ \cline{1-1}  & **75\%** & 0 & 0 & 0 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for the training of the ANN classifier with infrared imaging. Figure 7: Resulting projection of the classification regions obtained during the identification of uniform wear in gearbox gears by means of the ANN-based classifier: (**a**) training procedure and (**b**) validation procedure.