## 1 Introduction  
Bearings and gears are one of the most important components in the manufacture of wireless chargers. They affect the transmission efficiency, alignment error tolerance, charging power matching and thermal reliability of wireless charger performance [1, 2, 3]. For example, the shaft of the wireless charger is a key component, and the installation of the shaft is related to the service life and experience of the wireless charger. To enable more comfortable use and prevent the charger from being difficult to pull out, the modern charger is equipped with a rotating design, as the shaft causes scratches that reduce the rotation efficiency. In this regard, bearings and gears are incorporated into the shaft to improve the flexibility and efficiency of rotation and reduce friction. To improve the stability and durability of the wireless charger, it is also necessary to add a suitable heat dissipation function so that the heating mechanism of the charger's fan can include a heat dissipation plate and the connecting bearing and gear [4]. In this context, fault diagnosis of bearings and gears in wireless charging applications is necessary and useful. Deep Learning (DL) is a promising tool for automatic feature learning due to its deep architecture. It is widely used in natural language processing, state monitoring, speech recognition and other fields. In recent years, according to the rapid development of artificial intelligence technology, deep learning algorithms such as Stacked Auto-Encoder (SAE), Deep Belief Network (DBN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short Memory (LSTM) have been widely used in fault diagnosis. For example, [PERSON] et al. [5] stacked multiple AEs to extract features from raw bearing vibration signals without the need for professional technicians. However, due to the complexity of the original signal data, the AE method, which uses mean square error (MSE) as a loss function, is not very robust, so the performance is not good. [PERSON] et al. [6] proposed an improved depth model AE which is a combination of DAE and comparative AE (CAE). [PERSON] et al. [7] proposed feature extraction methods for bearing fault diagnosis: DBM and DBN. [PERSON] et al. [8] conducted experiments on gear and bearing fault data and verified that the selection of low-level feature domains has a profound effect on the deep statistical features of DBM. [PERSON] et al. [9] proposed PSO to optimise the DBN structure for fault diagnosis. [PERSON] et al. [10] proposed feature learning based on CNN using two sensors to detect vibration signals. [PERSON] et al. [11] proposed a hierarchical CNN method with an adaptive learning rate for bearing fault classification. [PERSON] et al. [12] transformed the fault diagnosis problem into an image recognition problem. [PERSON] et al. [13] used 1D data with a CNN model. The parameters of the model were determined using the PSO algorithm. Many results show that DL has strong scalability and generalisation capability compared to previously used machine learning algorithms (ML) such as Logistic Regression (LR), k-Nearest Neighbour (k-NN) and Support Vector Machine (SVM) and does not require manual feature extraction [14; 15; 16]. However, DL-enabled methods still have some limitations, such as: (1) source and target domains are evenly distributed; (2) the target domain has enough error data. Moreover, it is still difficult to meet both of the above requirements simultaneously. In practice, the accuracy of the features of the Deep Learning-based fault diagnosis model is inevitably affected by the number of fault samples and low quality. Moreover, the effectiveness of fault diagnosis cannot be guaranteed. Therefore, it is necessary to develop effective models to solve the problem of micro-fault diagnosis of label-free data in wireless charging devices. Transfer Learning (TL) is an efficient machine learning method that can use the knowledge in the corresponding source domain to solve the above challenges [17; 18].