## 2 Review of Issue Contents
To solve this issue, the transfer learning approach was employed to exploit the neural network trained in another (source) domain where enough fault data were available in order to improve the neural network performance of the real (target) domain. While there have been similar attempts of transfer learning in the literature to solve the imbalance issue, they were related to the sample imbalance between the source and target domain, whereas this study considered the imbalance between the normal and fault data. To illustrate this, normal and fault datasets were acquired from the linear motion guide, in which the data at high and low speeds represented the real operation (target) and maintenance inspection (source), respectively. The effect of data deficiency was studied by reducing the number of fault data in the target domain, and comparing the performance of transfer learning, thereby exploiting the knowledge of the source domain and the ordinary machine learning approach without it. By examining the accuracy of the fault diagnosis as a function of the imbalance ratio, it was found that the lower bound and interquartile range of the accuracy were improved greatly by employing the transfer learning approach. Feature extraction for bearing fault detection using wavelet packet energy and fast kurtogram analysis was presented in [5]. In this paper, an integrated method for fault detection of bearings using wavelet packet energy and a fast kurtogram was proposed. The method consisted of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of wavelet packet energy. Then, the analyzed signal was decomposed using wavelet packet energy, and the energy of each decomposed signal was calculated and selected for signal reconstruction. Secondly, the reconstructed signal was analyzed by fast kurtogram to select the best central frequency and bandwidth for the band-pass filter. Lastly, the filtered signal was processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach were illustrated and estimated using simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases showed that the proposed method can provide more accurate fault features compared with the individual fast kurtogram method. Fault diagnosis of industrial robot applications using machine learning was the main topic of [6], which dealt with exploiting generative adversarial networks as an oversampling method for fault diagnosis of an industrial robotic manipulator. Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that are hard to obtain, especially data collected from fault condition states; furthermore, without enough and appropriated (balanced) data, acceptable performance should not be expected. Generative adversarial networks (GANs) are receiving significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigated whether or not GAN can be used as an oversampling tool to compensate for an unbalanced dataset in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis was performed by taking into account six different scenarios for mitigating the unbalanced data, including classical under- and oversampling methods. In all cases, a wavelet packet transform was used for feature generation while a random forest was used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered.