# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 5 Experimental Validation  
### Experimental Setup  
This section uses the main motor of a tension leveler in a cold rolling plant as an example to validate the proposed algorithm's average accuracy, rapidity, and stability and analyzes the experimental results. The experiment was conducted on Python 3.8.3, using an RTX 4060 Ti GPU in a 64-bit Windows environment. The CNN was executed in the Keras 2.3.1 environment. The CNN-DS structure parameters and specific parameter values are shown in Table 3. The dual-channel neural network has approximately 2.35 million parameters, and each parameter typically occupies a floating-point number. In this study, we use 64-bit floating-point numbers, so 2.35 million parameters occupy about 18.8 MB of memory, which is much smaller than the memory available on the selected edge nodes. Of course, this is just the memory usage for the model parameters. The actual memory usage also includes the following: memory for intermediate activation values, memory for optimizer states, etc.  
The fault data set of the main motor is derived from the actual operation of a tension leveler leveling machine unit in a cold rolling plant. The main motor model is 1 PQ4 500-4 CM90-Z, the vibration sensor is RH104T, the signal acquisition bearing is a 6232 deep groove ball bearing, the temperature sensor is CA-YD-170, and the edge node uses a Jetson Orin NX development board. The main motor operates at a speed of 873 RPM. Temperature  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Parameter** & \multicolumn{2}{c}{**CNN-DS**} \\ \cline{3-5}  & & **Temperature** & **Vibration** \\ \hline \#1 Convolution-pooling & Number of convolution kernel & 128 & 32 \\  & Convolution kernel size & 3 & 3 \\  & Activation function & ReLu & ReLu \\  & Convolution stride & 1 & 1 \\  & Pooling size & 2 & 2 \\  & Pooling stride & 2 & 2 \\ \#2 Convolution-pooling & Number of convolution kernel & 64 & 64 \\  & Convolution kernel size & 3 & 3 \\  & Activation function & ReLu & ReLu \\  & Convolution stride & 1 & 1 \\  & Pooling size & 2 & 2 \\  & Pooling stride & 2 & 2 \\ \#3 Convolution-pooling & Number of convolution kernel & 32 & - \\  & Convolution kernel size & 3 & - \\  & Activation function & ReLu & - \\  & Convolution stride & 1 & - \\  & Pooling size & 2 & - \\  & Pooling stride & 2 & - \\ Fully connection (D-S) & Units & 256 & 128 \\  & Activation function & & \begin{tabular}{c} Belief Function \\ \end{tabular} \\  & & & \
\begin{tabular}{c} Plausibility Function \\ \end{tabular} \\ Output layer & Units & 8 + 1 \\  & Activation function & BPA \\ Loss function & - &
\begin{tabular}{c} Evidence Loss \\ 1.30 million \\ \end{tabular} \\ Total parameters & - & 1.30 million & 1.05 million \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and parameter values of the CNN-DS neural network.  
data are collected at a low frequency of 30 s per sample, with a sampling frequency of 0.03 Hz. The vibration data, sampled at a frequency of 16 kHz, pertain to drive-end faults. In addition to normal bearings, there are four types of faults: Ball Pass Frequency of the Inner Race (BPFI), Ball Pass Frequency of the Outer Race (BPFO), Fundamental Train Frequency (FTF), and Ball Spin Frequency (BSF). The specific number of sampling points is shown in Tables 4 and 5.