# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 10 Conclusions  
In this paper, the concept of a digital twin was presented to reflect the behavior and characteristics of the physical system in the virtual model. The digital twin model is a reference for real-time signals to monitor and detect faults in the AMB system.  
The neural network is employed for fault detection with a digital twin model. The various types of faults, like imbalance and misalignment, are detected by training the CNN using real-time signals. The experiment validated the efficiency of the suggested technique by the neural network using real-time signal data from the digital twin model.  
Using digital twin technology and neural networks, we observed several advantages, such as real-time monitoring, early detection of faults, improving system robustness, and optimizing maintenance methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Accuracy** \\ \hline Digital twin–CNN & 99\% \\ SVI+MCNN [41] & 98.6\% \\ AdaBoos [42] & 95\% \\ Fault dictionary [12] & 93\% \\ CNN-A [43] & 92.9\% \\ CNN-B [44] & 96.3\% \\ sdAE [45] & 95.01\% \\ LSTM-WDCNN [46] & 96.2\% \\ CNN-HMMS [47] & 98\% \\ ANN [48] & 95.14\% \\ DBN [45] & 94.07\% \\ LSTM [49] & 100\% \\ \hline \hline \end{tabular} The results reflect a high level of accuracy in identifying and classifying several types of faults, such as imbalance and misalignment.  
\end{table}
Table 4: How the digital twin–CNN accuracy compares with other algorithms.  
Figure 20: The impact of selecting multiple sensors on the digital twin model.  
Finally, integrating the digital twin technology with CNN algorithms presents a promising strategy for accurate and rapid fault classification, facilitating early repair and reducing operational downtime. This study established the way for further research in this area and presented new opportunities for improving the performance and dependability of AMB systems.  
Conceptualization, O.W.T. and Y.H.; methodology, O.W.T.; software, O.W.T. and K.Y.; formal analysis, O.W.T.; investigation, O.W.T.; resources, O.W.T. and K.Y.; data curation, O.W.T.; writing--original draft preparation, O.W.T.; writing--review and editing, O.W.T.; visualization, Y.H.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China, grant number 2018 YFB2000103.  
Data Availability Statement: Data are contained within the article.  
I would like to express my heartfelt gratitude to my wife, [PERSON], whose unwavering support, encouragement, and understanding have been the pillars of strength. Her dedication and patience have been a constant source of inspiration. I am also deeply thankful to my son, [PERSON], whose infectious enthusiasm and boundless curiosity have brought joy and perspective to my life. His presence has been a motivating force, reminding me of the importance of balance and family in the midst of academic pursuits. To my extended family, thank you for your love and encouragement. Your support has been instrumental in providing the foundation for my academic endeavors. I am truly blessed to have such a supportive network. This work is a testament to the collective support and love of my family, and I am forever grateful for their presence in my life.  
The authors declare no conflict of interest.