## 3 Experimental Implementation  
The AE signals from the bearings are obtained from the experimental testbed [3], as shown in Figure 5.  
The system contains two shafts: drive-end and non-drive-end shafts. These shafts are connected by a gearbox with a ratio of 1:1.51. In addition, rolling-element bearings are used to fasten both ends  
Figure 5: Testbed for collecting AE signals from bearings with compound faults: (**a**) Testbed diagram; (**b**) Real testbed components positions. (FAG NJ206-E-TVP2 Motion Industries, Birmingham, AL). The system's main power comes from a three-phase induction motor that operates at different speeds: 250, 300, 350, 400, 450, and 500 RPM. The AE data-acquisition facilities are attached to the bearings in the non-drive-end shaft. Seven bearing faults are determined with three different crack lengths (3, 6, and 12 mm), and the cracks are created by using a diamond bit cutter at seven different locations: outer raceway (BCO); inner raceway (BCI); roller (BCR); inner and outer raceways (BCIO); outer raceway and roller (BCOR); inner raceway and roller (BCIR); and inner raceway, outer raceway, and roller (BCIOR), as depicted in Figure 6.  
The data-acquisition system consists of a wideband AE sensor and PCI-2-based system, as described in Table 1. The system can capture the sampled AE signals at 250 kHz. The AE signals, which come from one normal condition (BNC) and seven defective bearings, are used to create three datasets. The first and second datasets are divided according to the crack length (3 and 12 mm), and the third dataset consists of all AE signals with three different crack lengths (3, 6, and 12 mm). In detail, each of these datasets is divided into three types of subsets: training subset, validation subset used for the training process, and testing subset used for evaluating our model performance. In the training process, besides the training subset, the validation subset is used during training to observe the model's learning ability. That helps avoid unexpected phenomena such as overfitting. The test subset and validation subset have the characteristics which are unseen by the model and are not used to update model parameters while training. The samples of the training subset are independent of the rotational speed to the validation and testing subsets, as shown in Table 2. The training subset consists of spectrum images created from the AE signals collected at shaft speeds of 300, 400, and 500 rpm, whereas the validation and testing subsets consist of the AE signals acquired at shaft speeds of 250, 350, and 450 RPM. In each of Dataset 1 and Dataset 2, the total number of samples used for the training process (training subset and validation subset) is \(N_{Class}\times N_{Samples}\times N_{Spend}=1440\) samples, where \(N_{Spend}=6\), \(N_{Class}=8\), and \(N_{Samples}=30\) (\(N_{Samples}\) is the total number of signal samples acquired at a specific bearing condition and specific shaft speed). Further, Dataset 3 consists of bearing fault samples in three degradation levels corresponding to three different crack sizes. Therefore, the total number of samples used for the training process is \(N_{Class}\times N_{Samples}\times N_{Spend}=3960\) AE signals, where \(N_{Spend}=6\), \(N_{Class}=22\) (three crack levels for each of the seven bearing fault types and one normal condition), and \(N_{Samples}=30\). In the testing process, 1200 samples for each class are used to evaluate the model performance. \begin{table}
\begin{tabular}{l l} \hline AE sensor (PAC WS\(\alpha\)) & Peak sensitivity (V/ubar): \(-62\) dB \\  & Operating frequency range: 100–900 kHz \\  & Resonant frequency: 650 kHz \\  & Directionality: \(\pm 1.5\) dB \\ \hline
2-channel PCI board & 18-bit 40 MHz A/D conversion \\  & 10M samples/s rate as only one channel is used (5M \\  & samples/s as two channels are used simultaneously) \\  & Dynamic range: \(>85\) dB \\  & Sensor testing: AST build-in \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of AE signals acquisition system.