## 3 Selected Research Results
### Machine Learning Methods
Figure 11: The architecture of the methodology from test object to state classification (**a**) and block diagram (**b**). Many algorithms were tested in this research, some of which are presented. The classification learner tool of MATLAB 2022a achieved a validation accuracy of 85.1% for the support vector machine (SVM) neural networks and a testing accuracy above 80.9% for multiple models (Figure 12). A confounding matrix represents the number of observations and correctly classified data (blue diagonal), and the brown-colored diagonal represents the incorrectly classified data in percentages. This is the true-positive rate (\(TPR\)) matrix, which represents the percentage of correctly classified observations for correct class 1; false-negative rates (\(FNR\)) depict the percentage of incorrectly classified observations for incorrect class 0. The quality of teaching, validation, and testing contributes to the accuracy of correct diagnosis in learning cases. The area under the curve (AUC) value measures the overall quality of the classifier. AUC values ranged from 0 to 1. Larger AUC values indicate better classifier performance. The closer the area under the curve (AUC) is to unity, the better the  
Figure 12: Test accuracy results for model 4.12 (a) and model 4.31 (b) classification of the used diagnostic parameters. model. The marker on the ROC plot shows the performance of the current classifier. The false-negative rate was calculated from the following relationship:  
\[W_{FN}=\frac{FN}{TN+FN} \tag{20}\]  
where \(FN\) is the number of false-negative classifications, and \(TN\) is the number of true-positive classifications. The marker in Figure 13 displays the values of the false-positive rate and true-positive rate for the models 4.12 (a,b) and 4.31 (c,d) classifiers. For example, a true-positive rate of 0.74 indicates that the classifier correctly assigns 74% of the positive class observations to the positive class. As can be observed from the ROC and AUC values (Figure 11c,d), the results obtained using the network are satisfactory. Figure 13: Test accuracy results for model 4.12 (a,b) and model 4.31 (c,d) classification of used diagnostic parameters for positive and negative classes. The algorithm was also trained for hyperparameter optimization using the classification learner tool [39]. By default, this tool tunes parameters using Bayesian optimization. In the application of hyperparameter tuning, a point is a set of hyperparameter values, and the objective function is the loss function or classification error. Figure 14 shows the estimated minimum classification error. The dark blue point corresponds to the observed minimum classification errors calculated for a certain number of iterations using the optimization process. In the Statistica program (Table 3), the testing quality was 80% for several networks, and the learning quality was 100% for the multilayer perceptron (MPL) 6-7-2 network. The values of the learning, testing, and validation quality, given (as a percentage) in Table 3, were close to \(\geq\)70%, and very close to \(\geq\)90% correlations were observed between the quantity of the diagnostic parameter values and the technical state classification values. Table 3 shows that the optimal network is MPL 6-7-2, comprising six input neurons, seven hidden layer neurons, and two neurons in the output layer. This network had the highest learning quality of 100% with the same quality of testing and validation. The testing accuracy can be improved by discarding less sensitive diagnostic parameters and testing more networks. The obtained values of the diagnostic parameters were assigned binary numbers based on limit values to a suitable state (1) or a state of damage (0). The results obtained in MATLAB and Statistica computer programs are similar [38; 40], which can be considered both satisfactory and encouraging for further research.