## 1 Introduction  
Machine maintenance is one of the most important fields in the industrial environment. In the normal working condition, the maintenance costs range from 15% to 40% of the total production cost [1]. Most manufacturing industries today use preventive maintenance, which replaces the machine parts based on a fixed schedule, to guarantee high maintenance safety. However, preventive maintenance cannot be considered as an effective maintenance method because the fixed schedule could not make full use of resources. This method can save costs ranging from 8% to 12% of the traditional maintenance cost and 40% of the reactive maintenance cost [2]. In the last decade, predictive maintenance has a more and more important role in manufacturing because of the improvement of the Internet of Things (IoT) and real-time data management. In the modern industrial environment, predictive maintenance focuses on the IoT and artificial intelligence (AI) platforms. In Figure 1, the maintenance includes data collection and signal processing to conducts early fault detection and diagnosis. Based on these platforms, the system can perform data collection and signal processing to conducts early fault detection and diagnosis by applying the concepts of data collection and management [3]. Some signal are popular in machine fault detection, such as vibration and acoustic signal. However, the acoustic signal is harder to collected, and more sensitive to noise, compared to vibration signal [4]. Various predictive maintenance schemes and AI models, which mostly use supervised learning, have been proposed lately [5, 6]. However, in the early phases when abnormal operations and fault machines rarely appeared in real-time scenarios, the initial fault vibration samples set is restricted. In the case of absent or limited fault samples, the training process for fault classification applications is difficult to conduct because of the imbalance of the input data. Therefore, data augmentation is necessary to increase the performance of the model training process when dealing with small fault datasets. An approach for the limited training data is transfer learning, where the target signal is created based on the source signal, which has the same distribution [7, 8]. However, the source signal using for transfer learning also requires the balance between the normal and fault data set. Therefore, data augmentation is also necessary for the transfer learning techniques when dealing with small fault datasets at the initial phase. In practice, the works of data augmentation in the time-series region are very limited and mostly focus on the traditional data transformation methods. The example of these methods is jittering [9, 10], scaling [9, 10], window slicing [10, 11], and flipping [10, 12, 13]. In these studies, the traditional data transformation methods do not significantly improve the accuracy of the model [9, 10, 11, 14]. Data augmentation, therefore, is not fully evaluated in time-series data analysis and fault detection applications. With the increase in the demand for fault detection applications in smart factories, the requirement for effective methods for data augmentation has been increasing [9, 15]. The general generative models produce outputs similar to the samples in the training dataset [16] by mimic the probability distribution function of the original data. The most popular generative method in data augmentation is the generative adversarial network (GAN) [17, 18]. The GAN algorithm is mostly applied in image processing and image generation. The main drawback of GAN is its instability during the training process, where the discriminator and generator try to fool each other [19]. Several studies have been conducted to improve GAN stability in the training process [20]. However, the limitation in GAN evaluation required human inspection, especially for picture generation and computer vision.