## 4 Results and Discussion
### Experimental Results  
This article selects a time-series length of 200 for a single sample, which is converted to a duration of 0.1 s. The data collection time for each group of experiments is 10 s, so each group of experimental data can obtain 100 image samples. There are also seven combinations of fault loads, and a total of 700 GAF image samples can be obtained. The segmentation ratio of the training and testing sets is 7:3, and the number of images in the testing set is 210. Two encoding image sets, GASF and GADF, were used separately and trained using the method proposed in this paper, with a training frequency of 100. [PERSON] was selected as the optimizer. The classification accuracy of the two encoding methods is shown in Table 3.  
From Table 3, it can be seen that there is not much difference in performance between the two on the training set, but on the test set, the accuracy of the GASF-encoded images is higher than that of GADF encoding. This may be due to GASF's ability to more comprehensively capture the correlation and similarity between signals by calculating their angles and summing them up. GADF uses angle difference in constructing the feature matrix, which may to some extent lose some important features of the signal. Therefore, in this article, only the t-distributed stochastic neighbor embedding (t-SNE) and confusion matrix of the GASF-encoded images are presented to characterize the specific classification situation, as shown in Figure 8 and 9. Figure 8 shows the visualization results after classification by the 'classifier'. From Figure 8, it can be seen that each type of fault is basically clustered together, and different types of faults are directly separated by a certain distance. There is no obvious overlap between different categories, indicating a high distinguishability of the features. Figure 9 shows the confusion matrix of the classification results. From Figure 9, it can be seen that the accuracy of the model proposed in this paper for identifying various  
Table 4 shows the diagnostic results before and after signal reconstruction. The model used for testing is the GAF-ResNet-MA model proposed in this article. By comparison, it can be seen that the diagnostic accuracy before reconstruction is 97.62%, and the diagnostic accuracy before reconstruction is 99.52%. This is because there are a large number of interference components in the original signal, and the diagnostic algorithm may not be able to accurately identify the fault characteristics, resulting in misjudments or omissions in the diagnostic results. However, in the reconstructed diagnostic results, due to the clearer and more stable signals, the diagnostic algorithm can more accurately identify the fault characteristics and provide correct diagnostic conclusions. Meanwhile, to further validate the superiority of the method proposed in this paper, five sets of comparative experiments were conducted. We compared the method proposed in this article with the GAF ResNet18 model, a support vector machine (SVM), a random forest model (RF), and a one-dimensional convolutional neural network model (1D-CNN). The same dataset was used for each algorithm to ensure the comparability of the results. The unique configuration of each model (e.g., hyperparameters, layer structure, etc.) is referenced from settings from other studies to ensure the reliability of the test. In addition, the data input for each model varies according to the needs of the algorithm. SVM and RF directly use the raw statistical features extracted from the dataset. 1D-CNN uses the one-dimensional time-series representation of the original data, CNN, GAF-ResNet18, and the proposed method to convert the time-series data into images through the GAF, capture time-dependent features, and then input them into the ResNet18 model.