# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 4 Experimental Verification
### Case Study 1: Application in CWRU Datasets  
#### 4.1.1 Experimental Setup  
The bearing datasets of CWRU have high data quality and obvious fault characteristics, so it is a commonly used standard bearing fault diagnosis dataset. In this section, three unbalanced datasets under different loads are constructed to testify to the recognition effect of the proposed method. In Figure 4, the test bench is composed of a 2 HP (1.5 KW) induction motor, a fan-end bearing, a driver-end bearing, a torque translator, and a load motor. By using EDM technology, single point faults with different depths were machined on the inner race, outer race, and rolling element of the test bearing. The fault diameters were 7, 14, and 21 mils, respectively.  
The vibration signals of the drive end bearing, collected under three loads of 0, 1, and 2 HP, are represented by A, B, and C, respectively, as shown in Table 2. In each load, it contains a normal signal, a rolling element fault signal, an inner ring raceway fault signal, and an outer ring raceway fault signal. Among them, the fault signal includes three states with fault diameters of 7, 14, and 21 mils, respectively. Therefore, each load constitutes 10 different data samples, and its time-domain diagram is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Fault Location**} & \multicolumn{2}{c}{**None**} & \multicolumn{2}{c}{**Ball**} & \multicolumn{4}{c}{**Inner Race**} & \multicolumn{4}{c}{**Outer Race**} \\ \hline Fault Diameter(mil) & 0 & 7 & 14 & 21 & 7 & 14 & 21 & 7 & 14 & 21 \\ \hline Class label & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline \multirow{2}{*}{Dataset A} & Training & 700 & 700 & 700 & 700 & 700 & 700 & 700 & 700 & 700 & 700 \\ (0 hp) & validation & 200 & 200 & 200 & 200 & 200 & 200 & 200 & 200 & 200 & 200 \\  & Testing & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{2}{*}{Dataset B} & Training & 2000 & 120 & 160 & 100 & 200 & 240 & 160 & 200 & 250 & 325 \\ (1 hp) & validation & 900 & 80 & 40 & 100 & 100 & 60 & 140 & 200 & 150 & 75 \\  & Testing & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{2}{*}{Dataset C} & Training & 6900 & 80 & 60 & 70 & 200 & 150 & 100 & 150 & 100 & 200 \\ (2 hp) & validation & 3000 & 20 & 40 & 30 & 50 & 100 & 200 & 150 & 200 & 100 \\ \cline{1-1}  & Testing & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bearing datasets.  
Figure 4: The motor-bearing test bench of CWRU: (**a**) real-life diagram of test rig; (**b**) structure schematic diagram of test rig.  
In the actual working environment, normal signals are often the vast majority of signals, while faults occur occasionally. In order to simulate this actual situation, three datasets were constructed, as listed in Table 2. In Dataset A, the number and proportion of samples for each health condition are the same, so Dataset A is a balanced dataset. In Dataset B, 3000 samples are collected for normal signals (1024 data points for each sample), 300 are collected for rolling element fault signals, 400 for inner race fault signals, and 500 for outer race fault signals. It is clear that the number of samples of normal signals is much larger than the other samples, and the proportion of sets on training, validation, and testing is also different in each state of the signal, thus constituting an unbalanced dataset. In Dataset C, the number of normal signals increases to 10,000, while the number of signals in other health conditions is reduced compared with Dataset B. Therefore, Dataset C is considered to be a highly unbalanced dataset.