## 4 Experiment Verification  
### Experimental Platform Construction and Data Description  
As an indispensable part of smart manufacturing for wireless charging, the stability of the wireless charging system is directly affected by the condition of the bearing. In this section, the validity and significance of the DTLCL method are tested using two different bearing datasets (dataset A and dataset B). Table 2 provides further details on the datasets.  
Case Western Reserve University (CWRU) provided dataset A [45]. The data files are in MATLAB format. In Figure 5, the experimental platform is depicted. There are four fault states in dataset A, namely normal fault (N), outer circle fault (OF), inner circle fault (IF) and rolling element fault (RF). Each fault type has three different fault diameters (0.007 inch, 0.014 inch and 0.021 inch). The vibration signals are recorded at a sampling rate of 12 kHz. The test bearings are loaded with four different motor speeds (1797 rpm, 1772 rpm, 1750 rpm and 1730 rpm) and motor loads (0 HP, 1 HP, 2 HP and 3 HP), which are considered four different working conditions. Each data set includes 1200 samples, 300 samples per condition and 100 samples per fault.  
Dataset B comes from the Intelligent Manufacturing Research Institute of Wuhan University of Technology and is shown in Figure 6a. The platform is driven by a SEW DRE100M4/BE5/HF/V/FI motor. The motor has the following specifications: 2.2 kW output power, 1425 RPM rated speed and 4 Nm rated torque. The roller bearing is a 6209-deep groove ball bearing with dimensions of 45 mm inside, 85 mm outside and 19 mm in width. Four vibration sensors record the vibration signals of the bearing at different positions of the motor drive side, the fan side and the pedestal, respectively, at different loads and speeds and use them as experimental data to diagnose bearing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Load/hp** & **Speed of Rotation/RPM** & **Fault Conditions** & **Samples Size** \\ \hline A & 0/1/2/3 & 1797/1772 1750/1730 & N/RF//IF/OF & 1200 \(\times\) 4 \\ B & 0/1/2 & 500/1000/1425 & N/RF/IF/OF GPF/GBTF & 200 \(\times\) 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data details of the bearing data set.  
faults with a sampling frequency of 12 kHz. Figure 6b shows the positions of the fault point sensors.  
Dataset B is divided into six operating states with no failure and varying degrees of failure severity. Light failure is 0.3 mm, medium failure is 0.6 mm and severe failure is 0.9 mm. The rated speed is 500 rpm, 1000 rpm and 1425 rpm, and the corresponding input current is 0.0 A, 0.1 A and 0.2 A. Each sample consists of continuously recorded 048 points. A total of 1200 samples were collected under six different operating conditions. There are 840, 240 and 120 samples for the training, verification and test data sets, respectively. The six fault conditions are: normal fault (N), outer circle fault (OF), inner circle fault (IF), pitting fault (GPF), rolling element fault (RF) and broken tooth fault (GBTF). For a mild fault, the fault diameter was set at 0.0018 inches, for a moderate fault at 0.0036 inches, and for a severe fault at 0.0054 inch. Table 2 shows the engine speed and load of different faults in data set A and data set B. Figure 7 shows typical original data collected from four vibration sensors at different positions in Figure 6a. The acquired data is then recorded and displayed using  
Figure 5: The experimental platform from CWRU.  
Figure 6: Experimental platform deployment. (**a**) Experimental platform. (**b**) 4 sensors in different positions. (**c**) Faulty parts of rolling bearings.  
MATLAB 2019. To fit the typical evaluation protocol for unsupervised transfer learning tasks, the training datasets consist of 90% labelled data in the source domain and unlabelled data in the target domain, and the test datasets contain the remaining unlabelled data in the target domain.