## 1 Introduction
This is because TL is a method for transferring data or features from the source domain to the target domain and improves model performance in the target domain with fewer data or features by using the source domain with more data or features. In general, TL methods are divided into model-based transfer learning (MTL), feature-based transfer learning (FTL), instance-based transfer learning (ITL) and relation-based transfer learning (RTL). FTL and MTL are two of the most popular methods. In MTL, the initialisation of the target domain model is usually pre-trained with data from the source domain. MTL is currently used in fault diagnosis and gives good results [19; 20]. FTL can change the properties of source and target domains by a domain adaptive method to identify a common potential space. If there is little or no labelling data for the target domain, FTL can be used [21]. The domain-adaptive method often consists of the maximum mean discrepancy (MMD). MMD is the distribution distance between the computation of the source domain and the target domain [22]. FTL includes shallow methods such as Transfer Component Analysis and Joint Distribution Adaptation, and deep methods such as Domain Confusion, Deep Adaptation Network, Domain Adversarial Neural Network and Deep CORAL (D- CORAL). Currently, FTL is used in the diagnosis of bearing faults. For example, based on the FTL method, [PERSON] et al. [23] assumed that there are some overlaps between the source domain and the target domain and proposed a Structural Corresponding Learning (SCL) method. Nevertheless, the robustness of each model is sometimes low. [PERSON] et al. [24] proposed training different transformation matrices for the source and target domains to achieve the goal of transfer learning. Based on the MTL method, [PERSON] et al. [25] proposed the TransEMDT method, which uses a decision tree to build a robust behaviour recognition model based on the labelled data. The RTL method is poorly researched and discussed in only a few articles [26]. There are other transfer learning methods. [PERSON] et al. [27] proposed the DANN method to add a confrontation mechanism to neural network training. [PERSON] et al. [28] from Google Brain extended DANN by proposing a DSN network. TL in smart manufacturing for fault diagnosis is still in its infancy [29]. The existing methods cannot be transferred between DL models created at different defect levels and mixed defects, so early micro-defect diagnosis and multiple defect diagnosis are not solved and are challenging. Due to limited space, this article focuses on early micro-defect diagnosis. For this reason, CNN has a good feature extraction capability. In the task of temporal sequence, LSTM can solve the problem of vanishing gradient caused by the gradual reduction in gradient without consideration. Considering the advantages of both methods, this study proposes a Deep Transfer Learning Network (DTLCL) based on CNN and LSTM for bearing micro-fault diagnosis with unlabelled or sparsely labelled data in wireless charging applications. The proposed method is based on DL, MTL, FDL and domain adaptation. First, a deep neural network (DNN) based on CNN and LSTM is built and pre-trained to learn transferable features, labelling the source domain data as significant error data. Second, by initialising the model of the target domain, MTL obtains a relatively good starting point. The network structure and number of neurons in each layer of the target domain model are identical to that of the DNN. Finally, FTL is used to learn invariant features in the source and target domains through Deep Domain Adaptation (DDA) [30]. The calculation of MMD loss by a Gaussian and linear kernel function can measure the distribution distance more effectively. The kernel MMD selection is designed to use the validation accuracy of the target model to assign an appropriate weighted voting (WV), which is a popular combination strategy.