# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 5 Conclusions and Future Work  
In this paper, an improved feature extractor structure has been put forward, and two popular networks, DANN and MCD, were selected for testing this revised feature extractor. The result has shown that with the help of the improved feature extractor, both DANN and MCD can well classify vibration signal data in fault diagnosis data sets, CWRU and XJTU, and these two models have achieved classification accuracies of 96.84% and 100%. Pseudo-label semi-supervised learning was also utilized in MCD, resulting in improved performance on average accuracy, achieving 94.19%. Finally, this research selected several popular models and estimated their transferability. The result has shown that the MCD with the improved feature extractor achieved the highest classification accuracy. DANN ranked in second place, but its accuracy curve oscillated significantly. Finally, pseudo-label semi-supervised learning was useful and achieved higher average accuracy on the test data set. This paper identifies several areas that could be investigated to improve these two networks, DANN and MCD, with the improved feature extractor. First, future research could focus on the multi-mode fault diagnosis data. This paper considers the vibration \begin{table}
\begin{tabular}{c c} \hline \hline
**Training Strategies** & **Best Accuracy** \\ \hline Baseline & 52.13\% \\ DASN & 69.30\% \\ DANN with the standard residual block & 94.40\% \\ MCD with the standard residual block & 98.17\% \\ DANN with the bottleneck residual block & 96.84\% \\ MCD with the bottleneck residual block & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transferability estimation of different models. signal data and conducts binary classification on vibration fault data sets. If multi-mode faults are under consideration, fault diagnosis networks can quickly and accurately identify the fault type and provide a solution. This can avoid property damage to the greatest extent and maintain the lasting stable operation of the machinery. Secondly, imbalanced data should be under consideration in the future. In this paper, source data is gathered from CWRU, and target data is gathered from the XJTU. Both of these are balanced data sets. However, imbalanced data is more common in actual production and life. When dealing with these data, the current network could achieve low performance and classification accuracy. So, how to improve these two networks for adapting imbalanced data is another future direction. Additionally, information loss should be considered in future research. In this paper, a bottleneck residual is utilized in the improved feature extractor. During the process of reducing the channels of input vectors, it could cause information loss. In addition, it has been observed that the proposed improvements to the feature extractor bring only marginal performance gains to already well-performing methods and applications. However, the applicability of this modified feature extractor to data sets with low predictability requires further examination. To evaluate its performance on such data sets, several strategies can be employed, including cross-domain validation, and transfer learning and domain adaptation. Cross-domain validation involves assessing the feature extractor's performance on diverse data sets from various domains and measuring its generalization capabilities. This approach enables researchers to predict its performance across different domains and determine if the modification consistently improves classification accuracy. On the other hand, transfer learning and domain adaptation involve fine-tuning the feature extractor using pre-trained models on established data sets to analyze its adaptation capabilities and performance in domains with different statistical properties. These strategies provide avenues for evaluating the modified feature extractor's performance and predicting its suitability for data sets with lower predictability.