## 2 Materials and Methods
### Sequential Features Generation  
The generation of sequential features was meticulously conducted across three distinct domains: time, frequency, and time-frequency. This approach was essential to capture the comprehensive characteristics of the signals under study. Each signal was systematically divided into 100 frames of a predetermined length. For each of these frames, a set of features was extracted, thereby converting the feature representation into sequential data. Consequently, each feature encompasses 100 sequences, effectively capturing the dynamic nature of the signal over time. In the time domain, feature extraction plays a pivotal role, especially in applications such as tool condition monitoring during milling processes. For this study, 11 unique time-domain features were meticulously extracted for each of the 11 signals. These features include the following [34; 35; 36]:  
1. Mean: Represents the average value of the signal, providing a basic measure of its central tendency;
2. RMS (root mean square): Offers a measure of the signal's magnitude, accounting for both positive and negative values;
3. Standard deviation: Indicates the variability or dispersion of the signal values from the mean; \begin{table}
\begin{tabular}{l l c c} \hline \hline \# & **Measured Parameter** & **Trial Duration** & **Sampling Rate (Hz)** \\ \hline
1 & Acoustic Emission & 27,999,960 & 5,000,000 \\
2 & X-axis Force & 700,000 & 200,000 \\
3 & Y-axis Force & 700,000 & 200,000 \\
4 & Noise & 700,000 & 200,000 \\
5 & Vibration & 700,000 & 200,000 \\
6 & Device Current & 30,000 & 50,000 \\
7 & Device Voltage & 30,000 & 50,000 \\
8 & Head Current & 30,000 & 50,000 \\
9 & Head Voltage & 30,000 & 50,000 \\
10 & Servo Current & 30,000 & 50,000 \\
11 & Servo Voltage & 30,000 & 50,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of variable configuration within the datasets. Shape factor: Describes the shape of the signal's distribution, offering insights into its characteristics;
* SNR (signal-to-noise ratio): Quantifies the ratio of signal power to noise power within the signal;
* THD (total harmonic distortion): Measures the distortion caused by harmonics in the signal;
* SINAD (signal-to-noise and distortion ratio): Combines noise and distortion measurements to assess signal quality;
* Peak value: Identifies the maximum absolute value within the signal, highlighting extreme events;
* Crest factor: Compares the peak amplitude of the signal to its RMS value, which is useful for identifying signal peaks;
* Clearance factor: Provides insights into the peakiness of the signal relative to its overall power;
* Impulse factor: Assesses the impulsiveness of the signal, indicative of sudden changes in the signal's behavior. For each of the 11 signals and the 11 features proposed above, the extraction process produced a matrix of size \(100\times 121\), resulting in a total of 121 potential sequential features from the time domain. In the frequency domain, a total of seven distinctive features were generated to enhance the analysis. These features were carefully selected for their ability to provide insightful information about the frequency composition and characteristics of the signal. The defined features include the following [37; 38; 39; 40]:  
1. Mean frequency: Provides an average frequency value weighted by amplitude, offering a centroid measure of the frequency distribution;
2. Median frequency: Represents the frequency at which the power spectrum is divided into two equal halves, indicating the balance of the frequency content;
3. Band power: Measures the total power within a specific frequency band, which is useful for understanding the energy distribution across frequencies;
4. Occupied bandwidth: Defines the bandwidth containing a specified percentage of the total power, highlighting the frequency range most significant to the signal's power;
5.