# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 3 Experimental Results
### Dataset Description  
To validate the effectiveness of the proposed method, this study uses two datasets for bearing fault diagnosis experiments: one is a bearing fault dataset collected from a self-designed test platform, and the other is a publicly available dataset provided by Paderborn University (PU). The following is a detailed description of these two datasets.  
**Dataset A: _Self-Designed Test Platform Dataset._** The dataset is collected from a self-designed bearing fault test platform (Figure 3a), consisting of a motor, coupling, rotor, bearing, and housing, using a 6204 deep groove ball bearing. The signal sampling rate is 10.24 kHz. The platform simulates various bearing fault conditions, including healthy (NF), outer race (OF), inner race (IF), rolling element (RF), and compound (CF) faults, as shown in Figure 4. During testing, the bearing operates at a fixed speed of 800 rpm and 1500 rpm, and vibration signals are collected by an accelerometer mounted on the bearing housing.  
Figure 2: Computing hardware core of proposed method.  
**Dataset B: _PU University Dataset._ The PU University bearing fault dataset is publicly available and widely used for mechanical fault diagnosis research. It was collected using an accelerated life test apparatus (Figure 3b) with a 64 kHz sampling rate. The dataset includes NF, OF, IF, and CF. Data are gathered under various load, speed, and fault conditions to simulate real-world industrial scenarios (Table 1), and include noise from environmental or equipment vibrations, ensuring diversity and generalizability for cross-domain learning.  
To be specific, the self-designed test platform dataset simulates bearing faults under controlled conditions with fixed speed and load, and is used for method validation and benchmark testing. The PU dataset, obtained from accelerated life testing, simulates  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Health States** & **Conditions Code** & **Speed** & **Load** & **Radial Force** & **Label** \\ \hline No fault & N09\_M07\_F10\_K004 & 900 & & 1000 & 1 \\ (NF) & N15\_M07\_F04\_K004 & 1500 & & 400 & 2 \\ Outer fault & N09\_M07\_F10\_KA16 & 900 & & 1000 & 3 \\ (OF) & N15\_M07\_F04\_KA16 & 1500 & & 400 & 4 \\ Inner fault & N09\_M07\_F10\_KI18 & 900 & & 1000 & 5 \\ (IF) & N15\_M07\_F04\_KI18 & 1500 & & 400 & 6 \\ Compound fault & N09\_M07\_F10\_KB24 & 900 & & 1000 & 7 \\ (CF) & N15\_M07\_F04\_KB24 & 1500 & & 400 & 8 \\ \hline \end{tabular}
\end{table}
Table 1: Fault types and specifications in the PU dataset.  
Figure 4: The bearing fault types: normal bearing (NB), ball fault (BF), outer fault (OF), inner fault (IF), and compound fault (CF).  
Figure 3: The main structure of the test platform: (**a**) the simulation test platform designed by ourselves, and (**b**) the accelerated life test bench from Paderborn University (PU).  
industrial bearing faults with varying loads, speeds, and fault types, offering greater complexity and diversity. By applying the proposed method to both datasets, the robustness and generalization are validated across different operating conditions and fault modes.