## 5 Conclusions and Future Works  
Product fault detection systems are important auxiliary systems for factories and have thus attracted considerable investment. However, as production modes change, many existing systems are becoming invalid. Upgrading existing product fault detection systems will enable factories to move into the era of Industry 4.0 at minimal cost. Existing methods, however, ask factories to completely rebuild their systems. Transfer learning can help factories retain their original systems, but some challenges must still be overcome. This paper proposes two approaches to transfer learning for the upgrading of existing product fault detection systems: (1) using a framework with a basic model and an add-on model and (2) designing an RBF-DNN to extract key features for model construction. We verified the effectiveness of the proposed approach using a real-world dataset. The proposed framework will assist factories in reducing costs associated with upgrading existing product fault detection systems.  
The above paragraph summarizes the research motivation and advantages of the proposed framework. However, our approach is subject to certain limitations. Since the framework of this paper is based on the concept of supervised learning, if insufficient error category data are collected by the factory, the framework will not be able to obtain enough training data, leading to system failure. In future work, we plan to include generative AI in this framework to simulate error category data. By using RBF-DNN as a generative AI recognizer, analyzing the critical features captured by this RBF-DNN would also improve the target framework. These additions would likely improve the practical applicability of the proposed framework.  
Conceptualization, C.-H.L. and Y.-C.C.; Data curation, C.-H.L.; Formal analysis, C.-H.L. and Y.-C.C.; Funding acquisition, Y.-C.C. and C.-T.S.; Investigation, C.-H.L. and Y.-C.C.; Methodology, C.-H.L. and Y.-C.C.; Project administration, Y.-C.C. and C.-T.S.; Resources, Y.-C.C. and C.-T.S.; Software, C.-H.L.; Supervision, Y.-C.C. and C.-T.S.; Validation, C.-H.L. and Y.-C.C.; Visualization, C.-H.L. and Y.-C.C.; Writing--original draft, C.-H.L. and Y.-C.C.; Writing--review & editing, C.-H.L. and Y.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council, grant number 111-2121-M-005-003 and 112-2121-M-005-006.  
Not applicable.  
The datasets presented in this article are not readily available because of a confidentiality agreement signed with Shuai Hao Spring Industrial Co., Ltd. Requests to access the datasets should be directed to the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Offline** & **Online** \\ \hline \multirow{3}{*}{Action} & Construction of an & Construction of a & \\  & RBF-DNN & neural network using & Running a neural \\  & & Matlab & network in Matlab \\ \hline Time cost & about 30 s & \textless{}5 s & \textless{}1 s \\ \hline \hline \end{tabular}
\end{table}
Table 9: Computational resource requirements for proposed method.