## 3 Description of Datasets
### The CWRU Dataset  
From the entire dataset [45], a subset of time series with drive end fault characteristics was used; data were collected at a sampling frequency of 12 kHz. Four condition types were considered: normal (healthy), ball, outer, and inner faults. Each fault type can vary in severity; conveniently, all three fault types may be characterized by having a fault diameter, and the dataset contains examples for diameters of 0.007\({}^{\prime\prime}\), 0.014\({}^{\prime\prime}\), and 0.021\({}^{\prime\prime}\), so there are ten conditions in total. Each bearing was tested under four different loads: 0, 1, 2, and 3 HP. The motor speed varies according to the applied load: 1797, 1772, 1750, and 1730 RPM. For each recorded data sample, the motor speed is constant. Table 1 summarizes the cases. The cases under specific loads of 0, 1, 2, and 3 HP are called sub-datasets #1, 2, 3, and 4, respectively; the entire dataset contains all sub-datasets.  
Figure 11: The scheme of the proposed method.  
Typically, there is only about 20 s of data for each type of signal, which limits training, validating, and testing. Accordingly, the raw vibration signals were segmented into overlapping frames. Considering that the rotation speed was approximately equal to 1800 RPM, i.e., 30 revolutions per second, we took signal segments with a duration of about 1/5 of a second. During this time, we would have six shaft revolutions, during which the defect should manifest itself six times. Since the signal sampling frequency for the CWRU dataset is 12 kHz, it was convenient to take segments with a length of 2500 samples. Each frame contained 2500 samples, which is equivalent to \(2500/12,000=0.208\) s, with an overlapping percentage of 50 %, i.e., 1250 samples, as shown in Figure 12.  
The number of frames obtained for each sub-dataset after the segmentation process is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class Number** & **Fault Type** & **Diameter (Inches)** \\ \hline
0 (no faults) & None & None \\
1 & Ball & 0.007 \\
2 & Ball & 0.014 \\
3 & Ball & 0.021 \\
4 & Inner & 0.007 \\
5 & Inner & 0.014 \\
6 & Inner & 0.021 \\
7 & Outer & 0.007 \\
8 & Outer & 0.014 \\
9 & Outer & 0.021 \\ \hline \end{tabular} \({}^{1}\) Load is 0, 1, 2, and 3 HP for each class.  
\end{table}
Table 1: Data classes in the CWRU dataset \({}^{1}\).  
Figure 12: Signal segmentation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sub-Dataset** & **Number of Frames** & \
\begin{tabular}{c} **Number of** \\ **Training Frames** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Testing Frames** \\ \end{tabular} \\ \hline
1 & 1066 & 747 & 319 \\
2 & 1258 & 881 & 377 \\
3 & 1258 & 881 & 377 \\
4 & 1260 & 883 & 377 \\ Entire dataset & 4842 & 3392 & 1450 \\ \hline \end{tabular}
\end{table}
Table 2: The number of frames for each sub-dataset in CWRU data.  
In the seminal work [45], each data record in the CWRU dataset was analyzed and classified into several categories based on their diagnostic interpretability, ranging from clearly diagnosable to non-diagnosable data. According to [45], ball faults were non-diagnosable using the three diagnostic methods described, highlighting the need for new approaches to achieve successful diagnoses for the datasets in this category.