# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Model Testing  
The experimental data were derived from vibration acceleration signals at four rotational speeds: 1797 rpm, 1772 rpm, 1750 rpm, and 1730 rpm. Separate datasets were created at each rotational speed, and the shaft rotational speeds in each dataset were assumed to be constant to exclude random fluctuations in the measurements. Details of the specific datasets can be found in Table 4.  
In order to validate the initial accuracy of the model under small sample sizes and variable operating conditions, we set the number of samples for all fault types in the training set for different speed conditions to 1, while the number of normal class samples was set to 10. This setup simulates the scenario of small sample imbalance in practical applications. The initial training was conducted using the discriminator of MTC-GAN to assess the model's ability to adapt to different operating conditions without increasing the data. The detailed hyperparameters for the MTC-GAN model are shown in Table 5.  
Figure 5: The vibration images for (**a**) the inner race fault, (**b**) the ball, and (**c**) the outer race fault. Figure 6: The LBP images for (**a**) the inner race fault, (**b**) the ball, and (**c**) the outer race fault. The preliminary fault diagnosis results of the bearing under a single working condition are shown in Table 6. To ensure the reliability and accuracy of the experiments, all experiments were repeated 20 times, and the average result was calculated after removing the best and worst results. The experimental results show that the model has achieved good accuracy through the preliminary data preprocessing method. The LBP operator is able to extract information about different types of vibration signals of the bearing. These data will be used as the benchmark before subsequent data enhancement. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Training Datasets** **(Number of Training Samples)**} & \multirow{2}{*}{**Testing Datasets** **(Number of Test Samples)**} & \multicolumn{3}{c}{**Classification Accuracy (\%)**} & \multirow{2}{*}{**Average Classification Accuracy (\%)**} \\  & & & **Inner Race** & **Outer Race** & & **Average** \\  & & **(Number of Test Samples)** & **Ball Fault** & **Fault** & **Fault (ORF)** & **Normal** & **Classification Accuracy (\%)** \\ \hline Dataset 1797 (15) & Dataset 1797 (125) & 94.72 & 92.76 & 95.84 & 100.00 & 95.83 \\ Dataset 1772 (15) & Dataset 1772 (162) & 92.28 & 91.33 & 94.23 & 98.56 & 94.10 \\ Dataset 1750 (15) & Dataset 1750 (160) & 93.49 & 91.56 & 95.33 & 98.40 & 94.70 \\ Dataset 1730 (15) & Dataset 1730 (160) & 93.78 & 93.20 & 94.35 & 100 & 95.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Initial diagnostic performance of the method under single operating conditions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Fault Type**} & **Shaft Speed (rpm)** & **Motor Load** (hp)** & **Number of Cycles** & **Number of Samples** \\ \hline \multirow{4}{*}{1} & Inner raceway & 1797 & 0 & \(\sim\)302 & 20 \\ \cline{2-5}  & Outer raceway & 1797 & 0 & \(\sim\)304 & 60 \\ \cline{2-5}  & Ball & 1797 & 0 & \(\sim\)305 & 20 \\ \cline{2-5}  & Normal & 1797 & 0 & \(\sim\)608 & 40 \\ \hline \multirow{4}{*}{2} & Inner raceway & 1772 & 1 & \(\sim\)300 & 19 \\ \cline{2-5}  & Outer raceway & 1772 & 1 & \(\sim\)301 & 58 \\ \cline{2-5}  & Ball & 1772 & 1 & \(\sim\)298 & 19 \\ \cline{2-5}  & Normal & 1772 & 1 & \(\sim\)1190 & 80 \\ \hline \multirow{4}{*}{3} & Inner raceway & 1750 & 2 & \(\sim\)296 & 19 \\ \cline{2-5}  & Outer raceway & 1750 & 2 & \(\sim\)295 & 58 \\ \cline{2-5}  & Ball & 1750 & 2 & \(\sim\)294 & 19 \\ \cline{2-5}  & Normal & 1750 & 2 & \(\sim\)1177 & 79 \\ \hline \multirow{4}{*}{4} & Inner raceway & 1730 & 3 & \(\sim\)293 & 19 \\ \cline{2-5}  & Outer raceway & 1730 & 3 & \(\sim\)293 & 58 \\ \cline{2-5}  & Ball & 1730 & 3 & \(\sim\)290 & 20 \\ \cline{2-5}  & Normal & 1730 & 3 & \(\sim\)1615 & 78 \\ \hline \hline \end{tabular}
\end{table}