# Novel Methodology for Condition Monitoring of
## 5 Results  
The diagnosis methodology proposed for the assessment of different levels of uniform wear in a gearbox transmission system was implemented in Matlab (Matlab 2017a, MathWorks Inc.), which was used for the processing of thermographic images, the estimation of statistical features, the reduction of features with LDA, and the ANN integration to perform the fault classification. As part of applying the proposed condition monitoring strategy, during the experimental evaluation of the different levels of uniform wear in gears, a set of infrared images was acquired for each case under study. Therefore, during the monitoring of each condition, each one of the tests was run for 90 min, acquiring a thermographic image every minute, and for each condition the last 50 acquired infrared images from the last 50 min were acquired and stored; therefore, a database of 200 samples (infrared imaging) was acquired. In this regard, it should be highlighted that only the last 50 infrared images were acquired due to the first 40 min being associated with the time that the gearbox transmission system requires to reach its thermal stability. Subsequently, after the experimental evaluation of each considered condition, a complete database of the thermal behavior of the gearbox transmission system was acquired. Thus, Figure 5a-d shows the last thermal image acquired during the monitoring of each considered condition; these images belong to the last acquisition obtained at the 90 th minute. In this regard, Figure 5a corresponds to the thermal monitoring of the gearbox when the gears are in a HTL state, Figure 5b corresponds to the condition of the gears with 25% uniform wear, Figure 5c represents the infrared imaging observed with 50% uniform wear of the gears, and Figure 5d shows the thermal behavior of the gearbox with 75% uniform wear of the gears. Additionally, from these Figures, it is possible to observe that there exist some differences that should be considered to provide the fault assessment; yet, although these thermal images show differences, an improved processing may lead to an accurate diagnosis. Figure 4: Proposed study cases for evaluating the gear in the gearbox: (**a**) HTL; (**b**) 25%, (**c**) 50%, and (**d**) 75% of uniform wear in the gears. Figure 5: _Cont._Once the thermography images have been acquired, all the acquired images are then characterized by obtaining their thermal matrices. Then, from each thermal matrix, a set of statistical features is estimated, which for this proposed work are named as thermal statistical features. Therefore, each considered condition is now characterized by a consecutive set of thermal statistical samples. Despite the high level of characterization provided by the set of thermal statistical features, not all of them contain the same representative information associated with the occurrence of uniform wear in the gears of the gearbox transmission system under assessment. In this sense, in order to preserve the best thermal statistical features extracted from the thermographic images, the estimated sets of statistical features are processed through a dimensionality reduction approach, which allows us to retain the most significant and discriminate information. In this regard, the LDA strategy is applied to the original set of statistical features estimated from the thermal matrices; as a result, a visual representation of all considered conditions in a 2D space is obtained. Figure 6 shows the resulting projection of the new extracted set of features obtained by means of applying LDA onto the original data sets that characterize the uniform wear conditions in the gears of the gearbox transmission system, such as a healthy gear, and 25%, 50%, and 75% wear. From this Figure, it is possible to observe that all the considered conditions appear separated from each other.