## 3 Smart Sensor Monitoring Approach
### Sensor Signal Feature Extraction Based on Autoencoder Architecture
The autoencoder was developed with the architecture described in Table 2, using a relu activation function in all the hidden layers and no activation for the output layer. The choice of the number of layers came from a trial-and-error procedure, in which the aim was to reduce the mathematical complexity and the reconstruction error at the same time. Since the signal pattern in the time domain is similar in shape, a simple architecture is capable of generalizing and reconstructing the signal. Figure 5: A typical autoencoder architecture consisting of encoder (extracting features from the input in the latent space) and decoder (reconstructing the original input from the latent representation). The training procedure was developed as follows. Once each signal, namely thrust force, torque, vibration, and acoustic emission RMS, was acquired as presented in the previous section for 60 drilling operations starting with a new tool, the dataset was randomly split into training data and test data subsets using a 75:25 ratio. An autoencoder was trained separately for each signal, and the reconstruction result obtained for one of the signals, the thrust force signal, is given in Figure 6 for one of the holes executed in the experimental test T4 with process parameters 7500 rpm--0.2 mm/rev. As demonstrated in Figure 6, the signal is reconstructed and filtered at the same time. Subsequently, only the encoding parts of each autoencoder were saved and used in the second step. In this second phase, the features for all signals were simultaneously extracted through their own autoencoders, and the 20 total extracted features were concatenated with the hole number, which provides information on the number of holes already processed by the tool, as proposed by [PERSON] in [14]. The feature extraction module is shown in Figure 7. \begin{table}
\begin{tabular}{c c} \hline \hline
**Signal** & **Number of Hidden Layers** \\ \hline Force (\(F_{x}\)) & (16, 8, 16) \\ Torque (\(T_{z}\)) & (16, 4, 16) \\ Acoustic emission RMS (\(AE\)) & (16, 4, 16) \\ Vibration (\(V\)) & (16, 4, 16) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Architecture used for each autoencoder. Figure 6: Original and autoencoder-reconstructed thrust force signal for one of the holes executed in the experimental test T4 with process parameters 7500 rpm—0.2 mm/rev. Figure 7: Feature extraction module: the outputs of the encoding parts of the four trained autoencoders are concatenated with the number of holes processed by the tool, realizing the feature’s vector. Correlation analysis was conducted to estimate the quality of the extracted features and their correlation with tool wear. For the latter purpose, the Spearman correlation coefficient was used, and the results are reported in Table 3. By defining as highly correlated the features presenting a correlation coefficient \(S_{s}>0.7\) and as well correlated the features with \(S_{s}>0.5\), it appeared that 74% of the features were highly correlated while the 17% were at least well correlated, so for the whole dataset more than 90% of the extracted features had at least a good relation with tool wear. This analysis showed that the acquired signals are all related to tool wear and that the unsupervised and automated feature extraction procedure proposed in this work was able to retrieve useful features to solve the task of tool wear estimation. Moreover, by observing the signal reconstruction performance in terms of the mean squared error in Table 4 and in terms of superposition with the original acquired signal in Figure 6, it is possible to notice how, using the extracted features, the autoencoder was able to reconstruct the time-domain signal without losing relevant information.