## 1 Introduction
Generalizing and automating the entire procedure for use in diverse circumstances is necessary to increase the work efficiency and diagnosis accuracy. Therefore, deep learning-based methods are used as an alternative. Deep learning-based methods are used for various purposes in several fields and are constantly evolving. In the PHM field, deep learning-based methods are also dominant in data-driven methods for fault diagnosis and prediction. Vibration signal data are also widely used for fault diagnosis using deep learning. After converting vibration signal into a spectrogram that can visualize the frequency band and time change of original signal, diagnosis performance can be dramatically improved by deep learning modeling such as convolutional neural network (CNN), a deep neural network widely used for visual image analysis. CNN is useful for time-frequency characteristic analysis because it extracts spatial information using image-specialized convolution calculations. By extracting high-dimensional information that cannot be distinguished by previous techniques through deep learning models, different health conditions can be easily distinguished, and end-to-end feature learning and classification are performed simultaneously. However, in the case of vibration signal data, there is a disadvantage in that many sensors must be attached to acquire data according to the characteristics of the object to be applied. In addition, problems such as optimization of the number of sensors and installation location may occur. Therefore, there are some studies that performed fault diagnosis and condition monitoring by building image-baseddeep learning models using thermal image data. The deep learning-based diagnosis model varies in its structure. Based on this, it can predict the health state of mechanical equipment. [PERSON] et al. [20] used time-frequency analysis and a deep residual network to perform fault diagnosis of a planetary gearbox under nonstationary running conditions. [PERSON] et al. [21] conducted a fault diagnosis of rolling bearings using a deep residual network. The accuracy of such deep learning-based diagnosis models is closely related to the number of collected data. When sufficient training data are available, deep learning-based diagnosis models with complex structures outperform other diagnosis models. However, if a small number of valid training data are collected directly from the task to diagnose faults, the reliability and accuracy of the diagnosis model inevitably decrease. In addition, training data and test data used for deep learning-based diagnosis models must have the same distribution. In actual industrial fields, the cost required to collect quality data is very high. Therefore, few data are actually collected from machine equipment under the specific work environment for diagnosis. The application of transfer learning can solve these problems in deep learning-based diagnosis models. Even if the labeled data are insufficient in the target domain to be diagnosed, there are data collected under different operating conditions. These different but related data can be used as the source domain for training a diagnosis model. The feature learning ability can be obtained through the source domain with sufficient labeled data and transfer learning. High diagnosis accuracy and stability are obtained by using source domain data for the target task. Transfer learning is essential for comprehensive fault diagnosis in industrial fields where identical process work is performed under various operating conditions. In recent years, studies using transfer learning have been actively conducted primarily for semi-supervised or unsupervised learning tasks, and few studies have conducted supervised learning tasks. [PERSON] et al. [22] transferred the structure and parameters of the VGG16 network trained from the ImageNet dataset for image recognition and used them for fault diagnosis of an induction motor, bearing, and planetary gearbox. [PERSON] et al.