# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 5 Conclusions  
In this study, we solved the concerns of wire rope defect diagnosis issues under various working conditions. Based on the LMA signal, this paper proposed a new CNN-transformer network to improve the overall diagnosis performance. The combination of a CNN and transformer was used for the first time in the wire rope defect diagnosis application field. In addition, the EMD data processing method was introduced to reduce the adverse impact of diverse noise signals. The image processing method was presented for the preparation of the wire rope dataset. TL architecture was proposed as the solution to improve the ability of domain adaptation. Consequently, through comparison experiments, the robustness and effectiveness of the DCNNT model and TL architecture were proven. The results indicate that the proposed method performs well and can balance detection accuracy, diagnosis speed, and computational cost factors. Then, visualization was conducted to understand why the proposed DCNNT model and TL architecture worked well in classification tasks. Finally, the MMD algorithm was used to analyze the transfer feasibility between different groups. In summary, DCNNT can achieve a better performance compared with other diagnostic methods in wire rope defect diagnosis activity, whilst being relatively acceptable to complete model training by a common GPU. TL architecture can avoid the time-consuming retraining procedure and solve the challenges of lacking labeled defect data.  
Although the results are encouraging, numerous limitations and challenges remain. Firstly, one of the challenges is how to apply the trained DCNNT model to another mechanical component that presents a considerable discrepancy in MMD, such as gearboxes and pumps. Second, generating a healthy degree, according to the diagnosis results, is difficult because this is the foundation for evaluating the entire mechanical system. In the future, some weighted-based and quantitative methods are recommended to realize the interaction between diagnosis and health evaluation models, such as the introduction of an analytic hierarchy process or the entropy weight method.  
Conceptualization, M.W. and J.L.; methodology, M.W.; software, M.W.; validation, Y.X.; formal analysis, M.W.; investigation, M.W.; resources, J.L.; data curation, M.W.; writing--original draft preparation, M.W.; writing--review and editing, M.W., J.L. and Y.X.; visualization, M.W.; supervision, [PERSON]; project administration, [PERSON] and Y.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Target Domain**} \\  & & **A** & **B** & **C** & **D** \\ \hline \multirow{3}{*}{Source} & A & 0 & 0.043 & 0.046 & 0.046 \\  & B & 0.043 & 0 & 0.038 & 0.044 \\ \multirow{2}{*}{Domain} & C & 0.046 & 0.038 & 0 & 0.033 \\  & D & 0.046 & 0.044 & 0.033 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: MMD values between different cross-domain groups.  
**Funding:** This research is supported by the National Basic Research Program of China, grant number is 2014 CB049401 and the Special Project of Industrial Cluster in National Innovation Demonstration Zone, grant number is 201200210400.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the results presented in this paper are not publicly available. Any access request should be sent to [EMAIL_ADDRESS].  
**Acknowledgments:** This research was supported by the National Key Laboratory of Intelligent Mining Heavy Equipment. The authors thank Luoyang BECOT Scientific Development Co., Ltd. (Luoyang, China) for providing the wire rope data used in this study. Furthermore, we appreciate the guidance from [PERSON], including wire rope feature analysis, research introduction preparation, and translation help.  
**Conflicts of Interest:** The authors declare no conflict of interest.