# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 2 Materials and Methods
### Experimental Data  
The publicly available dataset from CWRU [39], which is the bearing timing signal data collected by their experimental equipment shown in Figure 6. By artificial limitation, the motor shaft supported by the bearing does not rotate at a single approximate speed, its speed varies as the load changes [40], details of which can be known in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Structure** & **Parameter Setting 2** & **Output Size** & **Activation Function** \\ \hline Conv\_1 & k = 64, s = 16, p = 24 & 32 \(\times\) 64 & ReLU \\ MaxPooling\_1 & k = s = 2 & 32 \(\times\) 32 & - \\ Conv\_2 & k = 3, s = 1, p = 1 & 32 \(\times\) 32 & Meta ACON-C \\ MaxPooling\_2 & k = s = 2 & 32 \(\times\) 16 & - \\ Improved block with CBAM 1 & - & 32 \(\times\) 16 & ReLU \\ MaxPooling\_3 & k = s = 2 & 32 \(\times\) 8 & - \\ Improved block with CBAM & - & 64 \(\times\) 8 & ReLU \\ MaxPooling\_4 & k = s = 2 & 64 \(\times\) 4 & - \\ Improved block with CBAM & - & 128 \(\times\) 4 & ReLU \\ Adaptive AvgPooling & - & 128 \(\times\) 1 & - \\ Fully connected layer & in = 128, out = 10 & 10 & - \\ \hline \hline \end{tabular}  
* 1 Improved block with CBAM is the same structure as in Section 2.5.2, and the parameters are already listed in the text. 2 In Parameter Setting, “k” is kernel size, “s” is stride, “p” is padding, “in/out” is in/out features.  
\end{table} 
Table 1: Model Parameters. Figure 5: Structure of improved residual block. A portion of CWRU 12k drive-end bearing fault data, which a sampling frequency is 12 kHz, is selected for experiments in this paper, as shown in Table 3. For the drive-end bearings, which type are SKF6205, single-point faults were artificially implanted into them by using electro-discharge machining. The fault position is divided into three kinds: inner race fault (IRF), rolling body fault (BF), and outer race fault located at 6 O'clock (ORF), each fault position contains four fault sizes: 0.007 inches, 0.014 inches, 0.021 inches, 0.028 inches. Some of the data are unavailable, so we do not take all data of fault size 0.028 for the sake of experimental integrity. Therefore, in the experimental data, the bearing states under a certain load can be divided into nine types of fault states and one type of normal states, corresponding to the 10 time-domain vibration signals. To make full use of the original features of data while appropriately expanding data, a sliding window is used for each timing signal file with different loads. A sample will \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Motor Load** & 0 HP & 1 HP & 2 HP & 3 HP \\ \hline
**Approx. Speed** & 1797 r/min & 1772 r/min & 1750 r/min & 1730 r/min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The one-to-one correspondence between load and speed. Figure 6: CWRU bearing faults experimental equipment. be obtained when the sliding window length is set to 1024 data points and the step size is 224 data points per move, the process is shown in Figure 7.  
Sliding window to obtain the same length of each sample for 1024, containing approximately bearing work rotation contains about 2.5 turns of data points, feature information is sufficient, the calculation principle of the data points of a single turn as in Equation (9):  
\[N=60\times\frac{f}{R} \tag{9}\]  
where \(N\) is the number of data points collected from one working revolution of the rolling bearing, is the sampling frequency, and is the speed under the corresponding load. This method makes the sample sets, according to the ratio of 3:1:1. Taking 0 HP as an example, the specific fault conditions, quantity size, and fault type labels are shown in Table 4.  
Figure 8 shows the time domain history of the samples with different fault type labels, which reveals that the vibration signals of different fault types are distinguishable.