## 5 Conclusions  
This work proposes an intelligent transfer diagnosis method for bearing faults in the annealing kiln roller system on the basis of the Inception and LSTM modules to monitor the condition and diagnose the faults of the roller table and through shaft bearings under different working conditions. This method can overcome the influence of strong mechanical background noise interference and inconspicuous sample features and efficiently and accurately determine the health condition of bearings. The main conclusions of the paper are as follows:  
1. Considering the sparsity and kurtosis of vibration signals, the decomposed components using the ITD method can select and denoise strong mechanical noise, and CWT and histogram equalization can obviously enhance the time-frequency representations, clearly displaying the differentiated feature information on the time-frequency maps and promoting intelligent identification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **CNN-LSTM** & **DCORAL** & **DDA** & **The Proposed Method** \\ \hline Accuracy/\% & 86.30769 \(\pm\) 0.05015 & 91.84615 \(\pm\) 0.02794 & 93.84615 \(\pm\) 0.01738 & 99.84615 \(\pm\) 0.08406 \\ Time/min & 45.53 & 37.23 & 48.61 & 72.53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of intelligent fault transfer diagnosis for bearing system in annealing kiln.  
Figure 19: Loss function and accuracy curve of transfer learning training process.  
Figure 20: Identification confusion matrix of the bearings for the intelligent transfer diagnostic model. (**a**) Confusion matrix of training data of through shaft bearing. (**b**) Confusion matrix of the testing data of through shaft bearing.  
2. By combining Inception's multi-scale feature extraction and the LSTM's temporal relationship learning ability, an intelligent diagnosis framework for the roller table bearing is established to learn fusion features from the time-frequency representation. The experimental results of the measured data indicate that the proposed framework has excellent feature-extraction ability and can effectively and accurately identify the health condition of roller bearings.
3. By combining the MK-MMD method, an intelligent transfer diagnostic framework is established to identify the health condition using cross-domain condition samples. This method can reduce the distribution differences in vibration data and achieve the intelligent transfer diagnosis of roller table and through shaft bearings under different speed conditions in the annealing kiln roller system. Considering the plant scene and operation condition of the glass product line, the verification results from the experimental and measured data show that the intelligent transfer diagnostic model established in this work has high recognition accuracy for the health condition and fault characteristics of roller and through shaft bearings.  
Data curation, S.L. and T.L.; Methodology, K.Z.; Writing--original draft, Y.X. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the natural science foundation of Chongqing (cstc2020 jcyj-msxmX0334), Science and Technology Research Project of Chongqing Education Commission (KJQN202101119) Chongqing Doctor's Research Project (CSTB2022 BSXM-JCX0163).  
Not applicable.  
The data presented in this study are available in the article.  
We sincerely thank the reviewers for their constructive comments and the editors for their patient replies.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.