## 3 Results and Discussion
### Normalizing the Signal Amplitude  
By considering the amplitude averages in the architecture of the signal processing, the different findings from the previous section are combined. By normalizing the amplitudes in the time domain with the maximum amplitude in a preprocessing step, an increase of the success rate to 70.4% can be achieved. Detailed results are presented in Table 4. All settings and network parameters were selected analogously to the original reference network. It is also worth taking a closer look at the success rates of the individual measurement dates. Due to normalization, the success rates of measurement dates 2, 3, and 4 increase, whereas measurement date 1 achieves a lower success rate.  
Figure 5: Confusion matrix measuring date 3 evaluated with the reference network.  
Although measurement dates 1 and 3 have a comparable overall success rate, a closer look at the confusion matrices in Figure 6 reveals differences. Analogous to Figure 5, the number of functional nozzles is classified with state 1 and the number of clogged nozzles with state 2. When comparing the two measurement dates, the ANN assigns a majority of the output to class 1 for measurement date 1 and a majority of the output to class 2 for measurement date 3, irrespective of the actual nozzle state. In purely theoretical terms, the distribution of each measurement date should be balanced for an ideal success rate, as half of all test data represents a functional nozzle state and the other half represents a clogged nozzle state. In addition, the influence of the above-described normalization of the amplitude in the time domain is particularly interesting. At measurement date 3, when compared with the original condition (see Figure 5), 7.5% of all functional nozzles can be correctly classified as a result of the normalization. Without this modification, not a single functional nozzle could be recognized as such. However, the ANN still assigns the state clogged to more than 90% of the input values. In contrast, the success rate of the first measurement date is much more balanced compared to the third measurement date. Although the ANN increasingly assigns the state 1 (functional), 17.8% of all clogged nozzles can also be detected. Furthermore, in contrast to measurement date 3, both false positive and false negative errors occur.  
In summary, it can be stated that the performed normalization leads to an improvement of the overall success rate. The achieved 70.4% are in a range which is interesting from a technical point of view. However, the measurement date still influences the success rates considerably.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Date** & **Success Rate** & **(Min/Max)** \\ \hline Date 1 & 56.3\% & (Min: 46.6\%, Max: 64.4\%) \\ Date 2 & 84.3\% & (Min: 79.5\%, Max: 88.5\%) \\ Date 3 & 56.3\% & (Min: 50.8\%, Max: 62.5\%) \\ Date 4 & 84.8\% & (Min: 70.2\%, Max: 94.2\%) \\ \hline
**Average** & 70.4\% & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification success rates with the reference network after normalizing the acoustic signals in a preprocessing step in the time domain.  
Figure 6: Confusion matrices for measurement dates 1 and 3 after the signal was normalized in the time domain in a preprocessing step.