# Deep Residual Network Combined with Transfer Learning Based Fault Diagnosis for Rolling Bearing
## 4 Experimental Study
### Classification of Qianpeng Datasets  
(1) Dataset: The experimental data set in this section is obtained from the vibration signal data set collected by the QPZZ-II rotating machinery fault simulation experiment bench. The experimental platform and the faulty bearing are shown in Figure 4. The experiment uses a piezoelectric acceleration sensor model CA-YD-187 to pick up the bearing vibration signal. The acquisition system is based on the NI USB-4431 multifunctional high-precision data acquisition module from National Instruments, USA. The acceleration signal is first converted to an electrical signal by the acceleration sensor, then converted to a voltage signal by the charge amplifier, and converted to a digital signal by the data acquisition card. Finally, the code written by LabView software is linked to the data acquisition card for data acquisition and storage. The experimental bearing speed is 1188 r/min, the sampling frequency is 12 kHz, and the loading of the tested machine is zero. The bearing fault states in the experiment are divided into seven kinds, which are normal bearings, rolling element faults of different fault sizes, inner ring faults of different fault sizes, and outer ring faults of different fault sizes, as shown in Table 4. Examples of transformed images are shown in Figure 5, which shows that the light and dark distributions of the converted images differ for different fault levels on the same fault type. Figure 4: Experimental platform: (**a**) The test bench; (**b**) Photographs of defective components under study. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Part** & **Configured Version** \\ \hline \multirow{3}{*}{hardware} & CPU & Intel Core i9-10900 \\  & GPU & NVIDIA Quadro RTX 4000 \\  & Memory & 32G \\ \hline \multirow{3}{*}{software} & Operating System & windows10 64 bit \\  & Python & 3.7.11 \\ \cline{1-1}  & Pytorch & 1.7.1 \\ \cline{1-1}  & CUDA & 10.2.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental environment description. (2) Testing Accuracy: The image dataset is input to the trained network, the loss function of the model and the accuracy of the validation set are obtained after 20 iterations of training. The results are shown in Figure 6. The loss error of the network training shows that the ResNet34-TL model based on migration learning pre-training can drop rapidly at the beginning of the iteration and eventually stabilize. However, the loss function of the CNN network has been kept at a shallow value, and the accuracy from the validation set of the ResNet34-TL model shows higher and more stable accuracy. The already trained ResNet34-TL model performs well on training loss error and accuracy tests. The trained models were used for intelligent classification of the test set. All models were trained ten times to prevent random uncertainty in models, and the average of the ten tests was used as the final diagnostic result. The results are shown in Table 5; the ResNet34-TL model has the highest test accuracy of 96.29%. In this section, the ResNet34-TL model is sufficient to solve the accurate diagnosis of rolling bearing faults with fewer samples during network training and with only 20 iterations, which has application value for sizeable rotating machinery production work. It is also evident from the training time that the proposed network structure can substantially improve the recognition rate of fault types without affecting the speed of the actual network operation. In addition, the ResNet34-TL model has the lowest false alarm rate and missed alarm rate, which is 6.67% and 0.00%, respectively, and the ResNet34-TL model certainly has advantages.