## 4 Experimental Results  
This chapter presents experimental results obtained from offline and online detection. The first stage of developing the classifier was to perform measurements and prepare the network input vector (training and testing data). In the next step, an appropriate structure was selected 23-10-1, and training of neural networks and an analysis of effectiveness in offline classification were carried out. In the last stage, the trained network was implemented on a laboratory set-up, and online classification was carried out. In the proposed solution, the system is switched to sensorless mode based on the response of the comparative detector. The neural classifier, on the other hand, is only an indicator of the failure type. A flowchart of the described solution is presented in Figure 8.  
The complete control structure of the drive system with the detector and neural classifier is shown in Figure 9. Faults during the research were simulated in a software manner. The results are presented per unit. In the research, the same type of PI regulator with anti-windup with a correction parameter was used to control the speed and currents in the d and q axes in the rotor frame. The block diagram of the applied system is shown in Figure 10.  
The classifier effectiveness has been shown using confusion matrixes (Figure 11). Confusion matrixes are presented for neural classifier performance analysis obtained by offline classification. The exact parameters of the training and testing vectors are presented in Table 3. Based on the values presented in the table, it can be concluded that the network was tested for both interpolation and extrapolation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & **Value MRAS** & **Value SMO** & **Description** \\ \hline \(\omega_{\textit{err}}(k)\) & \(\omega_{\textit{err}}(k)\) & \(\omega_{\textit{err}}(k)\) & Reference speed value. \\ \hline \(\omega_{\textit{err}}(k)\) & \(\omega_{\textit{err}}(k)=\)\(\left|\omega_{\textit{max}}(k)-\omega_{\textit{err}  
Figure 8: Flowchart of detection, classification, and compensation system presented in the article. Figure 9: Control structure, including observer-based detectors and a neural classifier. Figure 11: Confusion matrixes with effectiveness obtained by MRAS-based classifier for training (**a**) and testing data (**b**) and SMO-based classifier for training (**c**) and testing data (**d**). Figure 10: Control structure, including observer-based detectors and a neural classifier. Based on Figure 11, it can be concluded that the SMO-based approach allows for higher efficiency. In the case of both classifiers, both for training and test data, 100% classification of signal loss was obtained. Most errors occur with scaling errors. This is largely because, for low speeds, the difference between the estimated and the measured speed is small. The detectability of this error increases with increasing speed. An important conclusion that can be drawn from the presented results is that the scaling error and signal interference are detected with high efficiency, but the network makes mistakes in distinguishing them. This is also confirmed by the high efficiency of indicating the operation without damage. The analysis of the influence of the input vector signals on the classifier output is presented using correlations in Figure 12. In the case of both classifiers, the inputs related to the speed are definitely more important. The q-axis current in rotor frame inputs is an auxiliary symptom. The same applies to the reference speed. The following results show sample transients for online detection and classification. First, the signal loss with and without load for different speeds for the classifier based on MRAS (Figure 13) is presented. In the neural classifier transients, the raw output of the neural network is shown \(NN_{Output}\)--and the output of the classifier, which is the rounded value of five samples of the network output--\(NN_{Classifier}\).