# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 3 Experimental Results and Discussion
### Diagnostic Results under Variable Load  
In an actual manufacturing environment, the load conditions are variable, which requires the fault diagnosis method to have high generalization ability. In CWUR's experiments, changes in load and speed are artificially linked, meaning that the work conditions of the bearings are different. The vibration data are acquired under different work conditions and lead to large distribution divergences which may cause the performances of fault diagnosis approaches to drop dramatically. This is usually known as the domain shift phenomenon [42], and its theory is shown in Figure 13.  
Figure 12: The confusion matrix of the fault diagnostic results (4 dB): (**a**) Confusion matrix of WDCNN; (**b**) Confusion matrix of the proposed model.  
Figure 13: The domain shift phenomenon theory.  
In studying this problem, the data used to train the model is usually referred to as the source domain, and the unlabeled data taken to test the model but relevant is the target domain [43], the goal is to generalize the fault diagnosis knowledge learned from the source domain to the target domain [44]. To obtain the generalization performance of the proposed model, experiments on bearing fault diagnosis under variable loads are required. The data samples of 0 hp, 1 hp, and 2 hp are taken as the source domain in turn, and then the data under the remaining loads are taken as the target domain. Ten dB noise is added to the samples in the target domain. Comparing the proposed model with the other three models, the average of the accuracy of the five repeated experiments as the experimental results that are shown in Figure 14. For example, \"1\(\rightarrow\)0\" means that train samples of 1 HP are taken as the source domain, and test samples of 0 HP as the target domain to validate and test, more domain details of the experiments can be seen in Table 8.  
As can be seen from Figure 14, the accuracy of 1 DCNN for fault diagnosis under variable load fluctuates greatly, and the generalization performance is inadequate, the lowest result is 80.70%. WDCNN performs similarly to MSCNN, and relies on the extracted multi-scale features, MSCNNN shows better generalization at 0\(\rightarrow\)2, as in the figure where the circle is located, achieving 90.125%. However, also because of the susceptibility to noise interference, even a small amount of 10 dB noise makes the fault diagnosis of MSCNN under variable load become worse than other models, at 1\(\rightarrow\)2, only 97.89% when the other three were over 99%. The average accuracy of the proposed model reaches 93.93% under variable load, which is 3.18%, 4.68%, and 3.32% higher than the other three models, respectively, indicating that our method can be applied for bearing fault diagnosis under variable loads because it has more superior generalization performance.  
Figure 14: Fault diagnosis results of different models under variable load domain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain Type** & **Source Domain** & **Target Domain** \\ \hline \multirow{3}{*}{**Description**} & Labeled signal & \multirow{3}{*}{Unlabeled signal samples under a single load} \\  & samples under a single load & \\ \hline \multirow{3}{*}{**Domain details**} & Train samples of 0 HP & Test samples of 1 HP & Test samples of 2 HP \\ \cline{2-3}  & Train samples of 2 HP & Test samples of 1 HP & Test samples of 0 HP \\ \cline{2-3}  & Train samples of 1 HP & Test samples of 0 HP & Test samples of 2 HP \\ \hline \multirow{3}{*}{**Objectives of the experiments**} & Generalize the fault diagnosis knowledge learned from source domain to target domain, diagnose samples in the target domain \\ \end{tabular}
\end{table}
Table 8: Domain details of the experiments.