## 4 Experiments
### Experimental Results and Analysis  
Rolling bearingss are one of the key components of rollings, whose main function is to withstand various workloads. In order to ensure the stable operation of the monitoring system under load changes, the fault diagnosis method for rolling bearings must maintain high accuracy, which puts strict requirements on the generalization ability of the fault diagnosis model. Therefore, this experiment will delve into the adaptability and accuracy of the fault diagnosis model MPIFNet proposed under different operating load conditions. In order to accurately evaluate the performance of the model, the experimental system systematically tested the fault diagnosis ability of the model under light load, medium load, and heavy load environments. Specifically, the proposed model was used to uniformly train and test the reconstructed datasets under four different workload conditions. Accuracy, F1 score, recall, and precision were used as the main evaluation indicators in the experiment, and the results are shown in Table 3.  
Figure 6: Time sequence diagrams of ten kinds of bearing vibration signals. The experimental results show that this model exhibits high accuracy under four different operating conditions, with an average accuracy of 98.00%, reflecting the excellent generalization ability and stability of the model. Especially under the conditions of a motor speed of 1730 Hp and a load of 2.206 kW, the model achieved optimal performance, indicating that under high load conditions, the model can effectively identify more complex fault modes, demonstrating excellent robustness and the ability to process complex data. For medium and light load situations, the model also demonstrated good diagnostic efficiency, ensuring reliability under normal operating conditions. Overall, through system testing and analysis under various load conditions, this experiment has validated the efficiency and adaptability of the model, demonstrating its potential for multi-condition fault diagnosis tasks in practical applications. This section aims to further verify the progressiveness of its effectiveness through comprehensive comparison with several classical convolutional neural network models. In order to ensure the comprehensiveness and fairness of the evaluation, benchmark models GoogleNet, ResNet18, and ResNet34 were used in the experiment. In order to conduct more refined comparisons, the infrastructure of ResNet18 was finely tuned according to the implementation requirements of the periodic two-dimensional transformation method in order to compare with this model. Specifically, in order to better adapt to task requirements and optimize model input processing, strategic adjustments were made to the infrastructure of ResNet18. This process includes using a periodic two-dimensional transformation method to convert data dimensions and fine-tuning the parameters of the first layer to enhance the ability to capture specific bearing fault data features. On this basis, a series of refined model variants were derived, specifically named ResNet18-L2, ResNet18-L3, and ResNet18-L4, where the number after \"L\" represents the number of convolutional layers retained in the original architecture. This hierarchical adjustment strategy aims to optimize the early feature extraction process through strategic hierarchical adjustment in order to improve diagnostic accuracy and generalization performance on datasets with multiple operating conditions. To ensure a fair comparison of the performance of different models, all models were trained and tested under the same training conditions. Each dataset is divided into training and testing sets in the same proportion. Note that in order to adapt to the input of the two-dimensional model, the sample sequence length is set to 1024 for the three classic models ResNet18, ResNet34, and GoogleNet in order to reshape them into a \(32\times 32\) two-dimensional matrix as inputs for the model.