# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 5 Conclusions and Future Work  
In this paper, we proposed a novel deep learning-based method that comprehensively considers data balancing, data features, and temporal information for fault diagnosis of  
Figure 10: A visualization of the same sample with different faults. electric screw presses. In practical applications, fault diagnosis systems must not only achieve high diagnostic accuracy but also meet the requirements for efficient computational load. To address this, we particularly focused on balancing these two aspects in the design of the DCBA model. The DCBA model combines the spatial feature extraction capabilities of convolutional neural networks (CNNs), the temporal feature modeling abilities of Bidirectional Long Short-Term Memory networks (BLSTM), and the key feature-focusing ability of the attention mechanism, resulting in excellent diagnostic accuracy. However, this high accuracy may lead to significant computational overhead. To make the DCBA model suitable for real-time applications, we reasonably simplified the network structure and adopted appropriate dimensionality reduction techniques and layer-pruning strategies to reduce the number of model parameters and computational complexity, ensuring a good balance between computational load and performance. Experimental results show that the optimized DCBA model not only performs excellently in terms of high-accuracy fault diagnosis but also completes fault detection tasks within a reasonable time, meeting the requirements of real-time fault diagnosis systems. Especially under noisy conditions and complex operating scenarios, the DCBA model maintains high stability and efficiency, demonstrating strong potential for real-time application. Therefore, we believe that the DCBA model not only performs outstandingly in experimental settings but also has broad application prospects. Through fault diagnosis experiments on the electric screw press system, we found that this method significantly improves the diagnostic prediction accuracy and reduces sensitivity to noise. Compared with existing centralized fault diagnosis methods, the proposed approach demonstrates superior performance. There are several aspects that warrant further research:  
1. This study primarily focuses on fault diagnosis of electric spiral presses, but the proposed method has broad application prospects. Therefore, further exploration of the method's application (in fields such as chemistry, biology, and construction) to validate its generalizability and practicality remains a highly meaningful research direction. Cross-domain experimental validation will not only enhance the applicability of the method but also help improve its adaptability in real-world industrial settings. 2. Introducing the attention layer into deep learning networks to improve interpretability is one of the highlights of our research. However, the reliability and stability of this method still require further improvement. Future research will focus on integrating expert knowledge, fault mechanisms, and other reliable traditional methods with deep learning techniques to enhance the robustness and accuracy of the model. By combining multiple techniques, we aim to effectively address the interpretability and reliability issues that deep learning methods face in specific fields. 3. The model we proposed currently identifies fault types known during the training phase, but its ability to recognize new fault types is limited, and the model's generalization performance significantly drops when faced with unknown faults. To address this issue, future research will focus on improving the model's ability to identify new fault types. We plan to incorporate techniques such as few-shot learning and incremental learning to further enhance the model's generalization ability in real-world scenarios, ensuring that it maintains efficient diagnostic capabilities even in the face of continuously evolving fault types. 4. The method proposed in this study currently remains at the theoretical level.