## 5 Conclusions  
Intelligent fault diagnosis for practical machine equipment is one of the most important tasks in the industrial field. In this paper, a multi-objective instance weighting-based transfer learning network is proposed for successful fault diagnosis. The proposed method is based on the transfer learning and instance weighting strategy that complementarily utilize two indicators, KLD and MMD, to minimize discrepancy between the two domains used for the transfer learning model. Through this, the accuracy of target diagnosis is improved by using data with different conditions under a general situation where only a small number of data were collected from the target system condition. Following conclusions were drawn according to the case study and comparison results.  
1. The proposed method outperforms the standard diagnosis models without transfer learning method.
2. It is verified that our multi-objective instance weighting strategy has higher performance than other optimization strategies used in transfer learning. The multi-objective instance weighting strategy can cope with deterioration of model performance due to dissimilarity between and within domains inherent in transfer learning process.
3. In particular, as the number of the training data in the target domain decreases, the improvement of the diagnosis accuracy and stability due to use of the proposed method increases.
4. The experimental setup of industrial spot welding that is actually carried out in automobile factories was used. Case study was conducted through the data collected from one accelerometer, with the realistic experimental conditions, such as difference in operating conditions and the number of data instances. It is confirmed that the proposed method has remarkable applicability for fault diagnosis in industrial sites.  
Because of the above advantages, the proposed method can apply to actual fault diagnosis where the collected data is limited. This means that high diagnostic performance can be achieved with low data collection cost. Furthermore, if the weight parameters foreach indicator is not the equal value, but is given according to the characteristics of the domain data to be applied, it can be widely used for other applications. In addition, through the process of repeatedly updating the weight parameters like the ensemble method, there is a possibility that it can be used for comprehensive failure diagnosis in industrial sites where many units of similar facilities exist.  
Conceptualization, K.L. and H.-J.C.; data curation, S.H. and V.H.P.; investigation, J.L., I.N. and S.W.L.; methodology, K.L.; validation, S.H., V.H.P. and S.C.; writing--original draft, K.L. and H.-J.C.; writing--review and editing, K.L., H.-J.C. and S.W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Chung-Ang University Graduate Research Scholarship in 2019. This work also supported by the Industrial Core Technology Development Program (\"Development of Prognostics and Quality Improvement Technologies for Enhancing Productivity and Yield Rate of Automobile Components by Minimizing Downtime of Manufacturing Robots/Process Equipment,\" No. 10073196) funded by the Ministry of Trade, Industrial and Energy, Korea. We thank the support from the BK21 FOUR (Fostering Outstanding Universities for Research) program funded by Ministry of Education of Korea (No. I20 SS7609062).  
Not applicable.  
The authors declare no conflict of interest.