## 5 Conclusions  
The objective of this work was to provide a generative AI-based methodology for synthesizing data that can serve as substitute for real-world data on damage in industrial rotating machinery. These synthetic data are used to train diagnostic algorithms based on deep learning and transfer learning. In order to achieve this objective, a cycleGAN generative neural network was employed to transform wavelet images associated with simulated signals into their experimental counterparts. The AI-based generative process was further validated by employing the MMD and FID metric. The examination of these indicators makes it possible to quantitatively determine the effectiveness of the generative network on the entire set of images investigated. Subsequently, three CNNs-based diagnosis models were trained utilizing the transfer learning technique, employing real, simulated, or synthetic data pertaining to damaged machinery. The diagnosis models were tested exclusively on authentic data acquired from a dedicated experiment carried out on a test platform featuring industrial bearings of medium size. Based on the evidence and analysis presented, it can be inferred that:  
* cycleGANs were found to be very efficient architectures for producing synthetic data in the form of CWT images, which may be utilized to augment or substitute real bearing fault data;
* the synthetic data generated by cycleGANs exhibiedt a higher degree of resemblance to real data when compared to the outcomes obtained from a simulation model. This assertion holds true for both the specific machine operating conditions that the generative algorithm was explicitly trained on, as well as for different conditions;
* the proposed methodology was able to generate data for working conditions that could be entirely lacking in the experimental activity. It is claimed that generative zero-shot capabilities arise also as a result of the cycle consistency inherent in the generative model;
* the synthetic data generated by the model were found to be effective for training diagnostic algorithms, which exhibited very high accuracy when evaluated using real data collected from the rotating machinery.  
Potential future advancements encompass the investigation of diverse radial and axial loading conditions, alongside the examination of distinct forms of bearing defect. Speeds more distant from the training speed will also be tested in the future. Furthermore, the objective is to perform a comprehensive examination by employing increasingly complex simulation models. Further research is essential to gain a comprehensive understanding of the underlying factors contributing to the ineffectiveness of the ResNet18 model when trained using synthetic data. Also, future developments will require the adoption of generative approaches available in the literature that will be measured under the conditions stated in this work to deliver a genuine and legitimate comparison to the scientific community. Ultimately, there are intentions to explore the boundaries in which data can be generated for operating conditions for which the generative model has not been explicitly trained as well as testing on different types of bearing.  
Conceptualization, L.G.D.M.; methodology, L.G.D.M.; software, L.G.D.M.; resources, E.B. and C.D.; writing--original draft preparation, L.G.D.M.; writing--review and editing, E.B. and C.D.; supervision, E.B. and C.D.; project administration, E.B. and C.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.