## 4 Results and Discussion
Namely, it is observed that there are two distinct rotation speeds (607 rpm and 997 rpm) where data could be effectively generated. These conditions indicate a notable alteration in the frequency distribution of the accelerometer data when compared to the initial condition of 877 rpm. This capability is characteristic of generative zero-shot learning and can be attributed to several factors. The utilization of an envelope-based pre-processing technique enables the identification and emphasis of fault features within spectra. The fault features exhibit a somewhat consistent shape across the various rotation speeds that are being studied. Hence, if the generative algorithm acquires the ability to manipulate those fault characteristics, it is conceivable that it can do so for varying rotation speeds as well, provided that the fault features in question have resemblance to those it has already acquired. Hence, it is postulated that the efficacy of this approach is constrained by its capacity to generate CWT spectra exhibiting fault features of comparable morphology when the operational parameters of the machinery undergo changes. Nevertheless, it is important to emphasize that the cycle consistency function plays a crucial role in facilitating a direct mapping between simulated and real signals. The \(\mathcal{L}_{cyc}\) function was specifically designed to facilitate the training of image translation models in the presence of unpaired images within the training dataset. In this scenario, it is observed that training images are paired. However, it is important to note that the algorithm does not explicitly learn the process of pairing images. Instead, the \(\mathcal{L}_{cyc}\) functions make the model focus on mapping an image to a different domain and subsequently reconstruct the original image, as presented in Figure 2 and Equation (3). This approach has the potential to significantly enhance the capacity to generate data under diverse operating conditions, exhibiting a generalized knowledge that can be extrapolated to novel and unfamiliar scenarios. Nevertheless, it is crucial to highlight that while validating the methods on speeds other than the training speed, it is important to consider the potential issues that may arise from moving away from the established 877 rpm, as it is uncertain how such deviations could impact the system's capabilities. The presence of uncertainty can be attributed to potential limitations within the cycleGAN model, as well as the choice of a demodulation band that may no longer adequately emphasize the transition from defects in CWT spectra. In conclusion, it is important to acknowledge that the diagnostic performances achieved by ResNet18 are poor when compared to those achieved by AlexNet and VGG16. This element is of significant interest and merits additional examination. The authors  
Figure 7: Results for real experimental test data for different diagnosis models trained on real, simulated and synthetic IR data: (**a**) accuracy; (**b**) recall; (**c**) training times. highlight that Resnet18 distinguishes itself from AlexNet and VGG16 by incorporating residual connections. These connections are also found in the cycleGAN architecture and may potentially contribute to Resnet18's ability to discern differences in synthetic data with respect to real ones. However, it is important to note that the aforementioned speculation is currently conjectural in nature and warrants additional research.