## 2 Review of Issue Contents
A nonparametric statistical test of hypotheses revealed that the GAN-based fault diagnosis outperformed both under- and oversampling classical methods. Novel models for induction machine diagnosis were described in [7], where the authors presented a hybrid finite element method/analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development of AI techniques can lead toward more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories, because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the end can become part of advanced diagnostic techniques. The benefits of analytical and numerical models for their speed and accuracy, respectively, can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the finite element model. In this paper, a hybrid model solved on multiple processors in a parallel fashion was presented. The results depicted that, by dividing the rotor steps among several processors working in parallel, the simulation time was considerably reduced. Under different degradation conditions, the complexity of natural oscillation of the piston pump will change, as stated by the authors of [8]. Given the difference in the characteristic values of the vibration signal under different degradation states, this paper presented a degradation state recognition method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and extreme gradient boosting (XGBoost) to improve the accuracy of state recognition. Firstly, ICEEMDAN was proposed to alleviate the mode mixing phenomenon, which was used to decompose the vibration signal and obtain the intrinsic mode functions (IMFs) with less noise and more physical meaning; subsequently, the optimal IMF was found using the correlation coefficient method. Then, the time domain, frequency domain, and entropy of the effective IMF were calculated, and the new characteristic values which can represent the degradation state were selected by principal component analysis (PCA) to realize dimension reduction. Lastly, the abovementioned characteristic indices were used as the input of the XGBoost algorithm to achieve the recognition of the degradation state. In this paper, the vibration signals of four different degradation states were generated and analyzed through the piston pump slipper degradation experiment. By comparing the proposed method with different state recognition algorithms, it was found that the method based on ICEEMDAN and XGBoost was accurate and efficient, with an average accuracy rate of more than 99%. Planetary gearboxes were the research object in [9]. The movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may render vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to effectively fix the axis gearboxes. A fault diagnosis method for planetary gearboxes based on an improved complementary ensemble empirical mode decomposition (ICEEMD)/time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) was proposed in this paper. First, the vibration signal of planetary gearboxes was decomposed into several intrinsic mode functions (IMFs) using the ICEEMD algorithm, which was used to determine the noise component as a function of the magnitude of the entropy, as well as to remove the noise components. Then, the time-frequency information entropy of the intrinsic modal function under the new decomposition was calculated and regarded as the characteristic matrix.