## 4 Results
### Conventional Machine Learning  
A measurement (5 s) consists of 256,000 data points. This array is too big to be directly inserted into the machine learning model and would lead to a long-lasting training as well as a badly trained model. Thus, the dataset is split into smaller samples. This means that out of each measurement, 1000 samples with 256 data points are created. As result of this, the dataset contains 12 classes with 10,000 samples per sensor. The number of usable samples increased by a factor of 1000.  
The results of the conventional machine learning model with one LSTM layer are shown in Table 2. Using all sensors for the training set the accuracy of the model is very good. However, this work has the focus on limited resources, thus the results of a single sensor in the set are of particular interest. The algorithm shows the best performance on the acceleration in the \(Z\) (radial) direction. The rollover of fault results in a radial vibration and acceleration, and this is the reason why the algorithm performs well with this sensor. The acceleration in the X direction is the lateral acceleration of the bearing position and its signal is similar to the radial acceleration but not equal. The change from directly radial to lateral measured acceleration results in a minor loss of information in the signal and performance of the algorithm. The training with the acoustic measurements from the microphone has the worst performance. The acoustic signal is very complex and can even contain ambient noise. In combination with little data, the algorithm is not able to extract the features belonging to the different faults.  
In general, adding a second LSTM layer has a positive effect on the performance of the conventional machine learning pipeline (see Table 3) except the training with the lateral acceleration (accX). In this case, the model fails completely and has a performance similar to guessing because the features are too complex to interpret. For the microphone, the radial acceleration and the combination of all sensors the more complex features extracted by the second LSTM layer result in a small increase in accuracy.  
Figure 13 shows the confusion matrix of the two-layer LSTM trained on the data from the microphone. The most difficult category to predict is the number five (medium wear inner ring). There some are classified as two (light wear outer ring), three (medium wear outer ring) and six (medium wear inner ring). The classification as a six may be the result of a small difference in wear. A difference in the rotational frequency results in a shift in the characteristic frequencies. With this shift, it is possible that the wear of the inner ring can be classified as outer ring wear. This explains even the rest of the confusion between classes two to seven.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Used** & **accX, accY** & **accX** & **accZ** & **mic** \\
**Sensor(s)** & **accZ, mic** & & & \\ \hline Train & 96.34\% & 90.98\% & 94.38\% & 70.48\% \\ accuracy & & & & \\ \hline Test & 94.77\% & 87.95\% & 90.61\% & 68.17\% \\ accuracy & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of the conventional LSTM model (one LSTM-layer).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Used** & **accX, accY** & **accX** & **accZ** & **mic** \\
**Sensor(s)** & **accZ, mic** & & & \\ \hline Train & 98.54\% & 8.78\% & 96.62\% & 77.98\% \\ accuracy & & & & \\ \hline Test & 95.89\% & 10.11\% & 92.43\% & 72.75\% \\ accuracy & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of the conventional LSTM model (two LSTM-layer).  
The best conventional model reaches a test accuracy of 95.9% (all sensors) and the worst, 10.1% (mic).