# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 4 Experiments
### Dataset Description  
1. CWRU dataset We choose the CWRU [28] dataset, a widely used dataset in the field of bearing fault diagnosis, to verify our proposed method. The experimental data are collected from the experimental platform, which is driven by a motor and sampled by acceleration sensors mounted on the bearing at the driving end and the fan end. In our experiment, we use the data of the driving end bearing with a sampling frequency of 12 kHz, which includes three fault categories, namely, the normal condition (N) and outer race fault (OF), inner race fault (IF), and ball fault (BF), where each fault category contains three damage levels of 0.007, 0.014, and 0.021 inches, respectively, The experimental data are collected under four different load conditions of 0-4 hp. These experimental conditions can simulate well the actual working conditions of different loads, different fault types, and different fault damage. The specific data labels are shown in Table 1. \begin{table}
\begin{tabular}{c c c} \hline
**Label** & **Bearing Status** & **Fault Diameter (inches)** \\ \hline
0 & N & - \\
1 & BF & 7 \\
2 & BF & 14 \\
3 & BF & 21 \\
4 & IF & 7 \\
5 & IF & 14 \\
6 & IF & 21 \\
7 & OF & 7 \\
8 & OF & 14 \\
9 & OF & 21 \\ \hline \end{tabular}
\end{table} 
Table 1: CWRU dataset information. 2. JNU dataset The bearing dataset of Jiangnan University [29] is designed specifically for bearing fault diagnosis and predictive maintenance research. The test bed is mainly composed of an acceleration sensor, motor, bearing, and conveyor belt. The dataset is collected at a sampling rate of 50 kHz and contains three different speed conditions of 600 RPM, 800 RPM, and 1000 RPM. The data types are divided into inner ring fault (IF), outer ring fault (OF), rolling element fault (BF), and normal state (N) to support various fault diagnosis and machine learning research scenarios. In the experiment, all faults were also cut manually. In this paper, we use the data of 600 and 1000 revolutions to conduct experiments. The specific data labels are shown in Table 2.
3. VMS dataset To address the insufficiency in data types of public datasets and to better verify the methods we proposed, we designed the Variable Mechanical Structure Bearing Experimental Platform and carried out data collection on it. The experimental platform mainly encompasses a motor drive system, rotor system, and support system. Among them, the motor drive system employs two SGMJV-04 ADD6S AC servo motors produced by Yaskawa of Japan, with a rated torque of 1.27 N-m, a power of 1.2 kW, a rotational speed of 3000 r/min, and a current of 2.7 A. The servo unit adopts SGDV-2R8A01 produced by Yaskawa of Japan, with a power supply of 3 PH AC 200-230 V 50/60 Hz, 2 A. The rotor system consists of three parts, namely, the high-speed rotor, low-speed rotor, and differential rotor. In this experiment, we utilized the data of the differential rotor. This part selects the bearing model NU202 EM, which is a type of cylindrical roller bearing, mainly constituted by the inner ring, outer ring, and cylindrical rolling elements between them. To simulate different fault types of the bearing, electrical discharge machining technology was used to cut the bearing, including inner ring faults, outer ring faults, and rolling element faults. When collecting data, we set the sampling frequency uniformly at 51.2 kHz and set the rotational speeds of the two groups of data to 677 RPM and 1797 RPM, respectively. Compared with the experimental platforms of the CWRU dataset and the JNU dataset, the position of the bearing to be tested on our experimental platform is farther from the drive end and passes through the support structure. Its mechanical structure can better simulate the characteristics of some more complex mechanical equipment, and the rotational speed differences of the two groups of collected data are greater, enabling a wider range of simulation of actual working conditions. This experimental platform is shown in Figure 6. The specific data labels are shown in Table 2.