# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 3 Results and Discussion
### The Application of the Machine Learning Algorithms
Generally, all of the tests indicated similar trends in significance, even though the particular results differed greatly from one model to another. Figure 5: Decision tree. It is seen in Figure 6 that the mean value from the T1 strain gauge was the most significant feature, which was confirmed by all four models applied. In turn, the kurtosis of the T3 strain gauge indications was found to be the least significant for the identification of the conveyor belt state. On the other hand, Laplacian Score method provided the highest values for test results, with one exception being the T2 Std.dev., while the ANOVA method gave the lowest values for the tested parameters with three exceptions. Figure 7a,b show examples of distribution graphs for the features found to be highly significant. In Figure 7a, the results for the T2 Max and T1 Mean are collected, while in Figure 7b, the T2 standard deviation and T1 Mean Square points are presented. In both diagrams, the areas of concentration of the points can be clearly distinguished. These points were used for state identification and damage classification for the analyzed belt. Figure 6: The ratings of the results for different features evaluated with different models. For better visualization of the performance of the tested algorithms, the results are also presented in the form of a confusion matrix in Figure 8. It represents the models that reached 100% of classification for all three cases, including the undamaged belt, the one with three defects, and the one with five defects. Each row represents the results of actual classification, and each column corresponds with a predicted class. For the cross-validation, the confusion matrix was calculated using the predictions for the validation observations. The cells located diagonally in the matrix indicate where the actual and predicted classes match. In order to check the performance of the classifier in individual classes, the function was selected, showing the true positive rates (TPRs) and false negative rates (FNRs). The TPR represents the percentage of correctly classified observations per true class, while the FNR shows the percentage of incorrectly classified observations per true class. The obtained confusion matrix confirmed the possibility of identifying the state of the conveyor belt from the analysis of signals generated by the strain gauge-based monitoring system. Figure 8: Confusion matrix for models that reached 100% classification. Figure 7: Examples of dispersion diagrams for significant features: (**a**) T1 Mean and T2 Max; (**b**) T1 Mean Square and T2 standard deviation. In order to compare the features of the obtained belt state observations and to illustrate the relationships between them, a parallel coordinate plot (PCP) is presented in \[X_{ms}=\sqrt{\frac{1}{N}{\sum}_{n=1}^{N}[x(n)]^{2}}, \tag{2}\]0 PCPs are widely recognized as a powerful technique allowing for the visual analysis of large sets reaching some millions of datapoints with multiple parameters associated with them [41]. The range scaling method was used to make the data fit into a certain pre-defined interval with the same minimum and maximum limits. The graph shows predictions for the _Coarse Tree_ model using eight features that exhibited the highest significance. The most significant features were specified based on \[X_{ms}=\sqrt{\frac{1}{N}{\sum}_{n=1}^{N}[x(n)]^{2}}, \tag{2}\]1 These features precisely show the ranges of values for three different states of the conveyor belt. The variables indicating a belt with five defects are marked in red. Apart from a dozen values, one distinctive area can be seen in the upper part of the graph. The values in the middle part have the greatest dispersion, and this area corresponds with the belt with three defects. The belt without damages is characterized by the variables in the lower part of the graph, which are marked in yellow and exhibited the smallest dispersion.