## 4 Experimental Results
Switching on the load degrades the properties of both the detector and the classifier. There are false positives and misclassification. The moment of switching on the load causes additional noise that has been misclassified. As the operating speed increases, the detection and classification systems improve, and the influence of motor load decreases. Motor load also reduces the accuracy of the current estimation in the iq axis. The results obtained for less significant damages, scaling error, and signal interference are shown in Figure 14. In this case, a significant advantage of the neural classifier over the comparative detector can be noticed. Signal interferences are classified using a constant signal, not pulses. On the other hand, the scaling error, which is switched on from the start of the system operation, is detected only after reaching the speed about \(0.15\omega_{N}\). The next two figures show the online classification based on the SMO. They confirm the higher efficiency obtained for this classifier. During signal loss (Figure 15), both the detector and the classifier almost do not make errors. The load also slightly affects the efficiency of the system. In this case, too, the increase in speed improves the performance of the system. This is especially important for the SMO-based classifier because the version \begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **Training Data** & **Testing Data** \\ \hline
**Number of samples** & 1,260,162 & 840,096 \\ \hline
**Speed values** & \(+/-0.1\omega_{\text{ref}}\), \(+/-0.2\omega_{\text{ref}}\), & \(+/-0.08\omega_{\text{ref}}\), \(+/-0.25\omega_{\text{ref}}\), \\  & \(+/-0.35\omega_{\text{ref}}\) & \(+/-0.4\omega_{\text{ref}}\) \\ \hline
**Load Values** & \(0.1\text{T}_{\text{N}}\), \(0.2\text{T}_{\text{N}}\) & \(0.15\text{T}_{\text{N}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of training and testing vectors in experimental studies. Figure 12: Correlation values between input vector elements and output vector in classifier based on MRAS (**a**) and SMO (**b**). used in the article does not work correctly for the lowest speeds. Motor start in sensorless mode, unlike MRAS, is impossible as it requires additional systems. Signal interferences are detected with high efficiency, while the quick detection of cyclic signal losses shows the dynamics of the detector's operation (Figure 16). In addition, the advantage of the SMO-based system is a much smaller impact of the motor load on the correct classification. Figure 13: Speed, MRAS detector, and neural classifier transients during signal loss without motor load (**a**) and with motor load \(0.15\text{T}_{\text{N}}\) (**b**). An important element of FTC systems is also the failure detection and classification time. Failure detection with a comparison detector in both cases--SMO and MRAS--is less than 0.0005 s. This time is not observable on the transients because every fifth sample was collected, and the sampling time was 0.0001. Whereas the classification times obtained for the testing vector under motor load conditions are presented in Table 4. Due to the method of data acquisition, the times presented in the table are approximate values. Figure 14: Speed, MRAS detector, and neural classifier transients during signal interference with motor load 0.15\(\mathrm{T_{N}}\) (**a**) and scaling error without motor load (**b**). Figure 15: Speed, SMO detector, and neural classifier transients during signal loss without motor load (**a**) and with motor load \(0.15\mathrm{T_{N}}\) (**b**).