## 1 Introduction
[23] used a CNN and transfer learning for fault diagnosis of rotating machinery under different working conditions. In addition, [PERSON] et al. [24] performed gear fault diagnosis using a deep CNN and transfer learning. The performance of transfer learning is determined by the size of the labeled target data and the discrepancy between the source domain and target domain [25]. Therefore, the process evaluating the attributes of the source domain instances that are useful for target domain task and using them as additional resources for model training effectively improves the performance of transfer learning. This is the main concept of instance-based transfer learning. Various optimization methods have been developed and used for conducting instance-based transfer learning. [PERSON] et al. [26] also conducted instance-based transfer learning using the dropout algorithm by removing target instances that are not well classified by a classifier. Dropout is a useful tool for reducing dissimilarity between domains when enough labeled data are available for each source or target domain. [PERSON] et al. [27] carried out the dropout of unsuitable source instances up to a certain number for a situation in which the number of target domain data is much smaller than that of the source domain. The combination of target dropout optimization, SAE-SVM, and ensemble learning resulted in the successful fault diagnosis of the ball screw. Dropout is a domain optimization technique suitable for situations where sufficient labeled data of the domain exist to drop out. If dropout is used when insufficient labeled data exist in each domain, model performance decreases due to a lack of data required for training model. Moreover, this is unsuitable for dealing with situations in which the dissimilarity between domains is large because all instances that are not removed have the same influence on model training. The instance-weighting strategy can solve these problems. It is noted that the accuracy of the method can be improved by focusing on the ensemble technique such as boosting. For instance, [PERSON] et al. [28] performed AdaBoost using a small size dataset collected from various operating conditions to predict the quality of friction drilling and to find the optimum parameters. Especially, [PERSON] et al. [29] proposed TrAdaBoost, which grafts AdaBoost applying instance-weighting strategy on to the transfer learning concept. It includes updating the weight vector due to the distribution difference between the source domain and target domain. [PERSON] et al. [30] expanded TrAdaBoost to deal with multi-source cases. The key idea is that, among the diff-distribution training instances from different domains, instances that are not well fitted to the target domain have low training weights and become less influential in later training stages. In this paper, we propose the multi-objective instance weighting-based deep transfer learning network (MOTL). The proposed method is applicable not only for fault diagnosis of general cases but also for fault diagnosis when data are collected under different operating conditions in a practical range. A significant distribution difference between source and target domains exists, and only a small number of labeled data are collected from each domain. In this method, source instance weights calculated using Kullback-Leibler divergence (KLD) [31] and the maximum mean discrepancy (MMD) [32] (two indicators evaluating the distribution difference between domains) and a large volume of source domain data are used as auxiliary training resources to train a deep residual network. Afterward, the structure and parameters of the pre-trained model are transferred, and fine-tuning is conducted using a small number of labeled target data. In addition, a spot-well case study using an industrial robot is conducted to verify the applicability to actual industrial fields, which is one of the significant advantages of transfer learning. The main contributions of this paper are summarized as follows. 1.