## 11 Discussion  
Results of the comparison of the models CNN, LSTM, and BiLSTM for classification of pieces of the MFCC concerning the spectrogram were presented in Tables 9 and 10. These results confirm that our method of pre-processing the vibration signal attains a 2D representation that enables the DL methods to classify many faults accurately. In contrast, using the spectrogram as input features results in lower accuracy when the number of faults increases (Table 10). The accuracy attained by the three DL models is very high for both applications, even when the number of faults is also high. In general, the variance of the CNN model during cross-validation can be larger than the variance in accuracy of the LSTM and BiLSTM, especially when dealing with vibration signals recorded far from the fault location. The average accuracy of the CNN and BiLSTM is almost similar for vibration signals near fault locations. In contrast, the average accuracy of the LSTM can be slightly lower. The CNN has the advantage of requiring the lowest computation time during training. In contrast, the BiLSTM requires the highest computation time during training. A qualitative representation of the cluster structure of the learned representation by the deep learning approaches is feasible. Such a representation can be attained using \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Signal** & **Accuracy MFCC** & **Accuracy Spectrogram** \\ \hline \hline  & A1 & 99.82 & 96.74 \\ CNN & A2 & 99.59 & 95.55 \\  & A3 & 99.40 & 93.98 \\ LSTM & A1 & 99.75 & 97.77 \\  & A2 & 99.63 & 96.78 \\  & A3 & 99.51 & 95.91 \\  & A1 & 99.82 & 97.74 \\ BiLSTM & A2 & 99.74 & 96.45 \\  & A3 & 99.58 & 96.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of Performance of tested models obtained during the 10-fold cross-validation, classifying pieces of the feature array extracted from the MFCC and Spectrogram in the reciprocating compressor. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Signal** & **Accuracy MFCC** & **Accuracy Spectrogram** \\ \hline \hline  & A2 & 99.76 & 98.86 \\ CNN & A3 & 99.64 & 97.36 \\  & A4 & 99.96 & 98.61 \\  & A2 & 99.81 & 99.92 \\ LSTM & A3 & 99.81 & 99.80 \\  & A4 & 99.91 & 99.73 \\  & A2 & 99.89 & 99.82 \\ BiLSTM & A3 & 99.89 & 99.86 \\  & A4 & 99.96 & 99.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of Performance during the 10-fold cross-validation for classifying pieces from the MFCC and Spectrogram in the MFCC and Spectrogram in the MFCC and Spectrogram in the MFCC and Spectrogram in the centrifugal pump. the t-distribution stochastic neighbor embedding (t-SNE) [64]. The t-SNE enables the visualization of high-dimensional data by constructing a mapping from the original space to a lower-dimensional space map. An example is presented for the reciprocating compressor. Results are shown in Figure 15a,b for the A1 channel of the vibration signal. In the case of CNN, the activation from two layers is extracted: the initial convolution layer and the softmax layer. The clusters representing each of the 17 fault classes are well separated in the softmax layer, which explains CNN's high classification accuracy. Concerning the LSTM and BiLSTM neural networks, the visualization was constructed using the learned features of the LSTM or BiLSTM layer. The cluster structure for the LSTM neural network is shown in Figure 15c. Similarly, in Figure 15d, the cluster structure for the BiLSTM neural network is shown. The clusters for the BiLSTM neural network are more compact than the clusters attained by the LSTM neural network. However, the clusters attained by both models tend to have a circular shape and are separated in this projection space. Concerning comparing other methods reported in the literature (see Table 11), some attain a classification accuracy of 100% with a complex architecture for the CNN and a small set of faults. In the research in [PERSON] et al.