## 3 Experiments and Comparisons
### Experimental Results Analysis
#### 3.4.3 Analysis of Ablation Experiment Results  
To evaluate the effectiveness of the ChebyAT-based fault diagnosis method, this paper used the classic ChebyNet as a control group and utilized the original model alongside three improved models, as detailed in Table 4, for comparative analysis. Four evaluation metrics--accuracy, precision, recall, and F1 score--were utilized to assess model performance [21]. The precision, recall, and F1 score of the classes were weighted and averaged according to the number of samples in each class. The specific descriptions of each model are as follows:  
(1) ChebyNet: Used as a control group with no modifications to its network structure.  
(2) S-ChebyNet: Introduces symmetry processing improvements based on ChebyNet to evaluate the effectiveness of this enhancement and serves as a baseline to assess the impact of adding a GAT and the multi-task collaborative strategy.  
(3) S-ChebyA: Integrates a GAT into the improved ChebyNet structure from the S-ChebyNet to evaluate the effectiveness of the GAT and serves as a baseline to assess the impact of the multi-task collaborative diagnosis strategy.  
(4) ChebyAT: Adds the multi-task collaborative diagnosis strategy to the S-ChebyA network. As an experimental group, it is compared with the S-ChebyA to verify the effectiveness of the multi-task collaborative diagnosis strategy.  
The ablation experiment results are shown in Table 4. Compared to the classic ChebyNet model, the F1 score of the S-ChebyNet model, enhanced by symmetry processing, improved by 4.18%. This indicates that symmetry processing effectively addresses the imbalance in information transmission resulting from differing data distributions under variable working conditions, ensuring the fair integration of information and reducing the misclassification of categories with fewer samples, as well as those with more samples. The F1 score of the S-ChebyA model, which incorporates a multi-head graph attention mechanism built upon the S-ChebyNet, increased by 5.73%. This improvement indicates that the GAT effectively addresses the local feature extraction limitations of the ChebyNet, balances global and local features, mitigates misjudgment due to information loss, and enables the model to accurately identify samples through a comprehensive analysis of fault features. The F1 score of the ChebyAT model, which incorporated the multi-task collaborative diagnosis strategy, increased by 5.73%, reaching 99.75%. This indicates that the implementation of the multi-task collaborative strategy effectively enhanced the model's ability to distinguish similar faults under varying working conditions and significantly improved its generalization capabilities. The evaluation indicators indicate that improvements in symmetry processing, the integration of the GAT, and the inclusion of the multi-task collaborative strategy have significantly enhanced the model's performance in fault recognition under variable working conditions.  
The results of the ablation experiment clearly demonstrate the interaction and mutual enhancement of improvements in symmetry processing, the integration of the GAT, and the inclusion of the multi-task collaborative strategy in enhancing the model's fault recognition performance under variable operating conditions. The enhanced symmetry processing supplies the model with a more balanced and comprehensive data input. The integration of the GAT further improves the model's feature extraction capability by considering both local and global features, facilitating balanced extraction. The multi-task collaborative strategy enhances the model's generalization capability under varying working conditions by simultaneously addressing node classification and reconstruction tasks, leveraging its powerful feature extraction capability. These three modules collaborate during the stages of data input, feature extraction, and fault identification, mutually reinforcing each other and collectively enhancing the model's fault identification performance under variable operating conditions.