# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 3 Experimental Results
### Performance Analysis
The average accuracy of the experimental results is shown in Table 3. The comparative analysis is as follows:  
* _Advantage of Time-Frequency Domain Data_: Comparing Method 1 and Method 2, the average accuracy for time-frequency domain data increased by 2.8% (from 85.7% to 88.5%). This indicates that time-frequency data contain richer fault feature information, helping the model better capture fault patterns. Similarly, Method 4 (time-frequency domain) is 2.4% higher than Method 3 (time-domain), and Method 6 (time-frequency domain) is 3.4% higher than Method 5 (time-domain). The advantage of time-frequency data is more prominent in complex tasks. * _Effect of Distillation_: Comparing Method 1 and Method 3, distillation improved the average accuracy of the time-domain data by 2.6% (from 85.7% to 88.3%). Similarly, comparing Method 2 and Method 4, distillation improved the average accuracy of the time-frequency domain data by 2.2% (from 88.5% to 90.7%). Distillation enhances the model's generalization ability through knowledge transfer and regularization. * _Effect of Domain Adaptation_: Comparing Method 3 and Method 5, domain adaptation increased the average accuracy for time-domain data by 1.8% (from 88.3% to 90.1%). Similarly, comparing Method 4 and Method 6, domain adaptation improved the average accuracy for time-frequency domain data by 2.8% (from 90.7% to 93.5%). Domain adaptation effectively mitigates the data distribution mismatch, especially for time-frequency data. Figure 8: Time-frequency results of bearing fault after applying the STFT. Figure 7: Time-frequency results of bearing fault after applying the CVT. * _Cumulative Effect of Method Combinations_: The proposed method (Method 6) achieved the highest average accuracy of 93.5%, outperforming the baseline Method 1 by 7.8%. The combined effect of these techniques shows that distillation and domain adaptation synergistically maximize model performance when applied to time-frequency domain data. * _Generalization Capability_: To evaluate the generalization ability, the proposed method was tested on two datasets: Dataset A (Self-Designed Test Platform Dataset) and Dataset B (PU University Dataset), achieving 93.5% and 94.2% accuracy, respectively. This consistency across datasets with varying operational conditions (e.g., speed, load) demonstrates robustness to domain shifts. Furthermore, we evaluated performance under different noise levels (SNR 0 to 10 dB) and fault severities (1 mm to 4 mm defects), showing less than 3 % accuracy drop, which underscores its adaptability. Moreover, the integration of domain adaptation further ensures feature invariance across domains. Therefore, time-frequency domain data are more suitable for fault diagnosis tasks as they provide richer feature information, significantly improving model accuracy. Distillation enhances the model's generalization ability, especially in data-limited or complex tasks. Domain adaptation effectively addresses data distribution mismatches, particularly in cross-domain diagnostic tasks. The proposed method, with an average accuracy of 93.5% and 94.2% with different datasets, proves to be the optimal approach for bearing fault diagnosis in practical industrial scenarios.