## 1 Introduction
Also, several techniques are available for the re-utilization of models pre-trained for image recognition [29] and sound recognition [49; 50] in the field of rotating machinery diagnostics. Generative Adversarial Networks (GANs) [51; 52; 53] could be intended in a broad sense as TL approaches. In the domain of IFD, GANs are employed to construct generative models capable of producing synthetic data resembling the patterns observed in operating machinery [54; 55; 56; 57; 58; 59; 60; 61]. Artificially produced data serve the purpose of enhancing the training dataset for IFD models for improving the performances and the feature extraction capabilities of the latter. The concept of GAN leverages the underlying mechanism employed by stacked autoencoders and variational autoencoders, which have been employed in the past to produce high-level features for training machine learning classifiers [62] and to diagnose rotor-bearing systems [63; 64]. In recent years, a growing body of literature has drawn attention to the potential of GANs in the field of rotating machinery fault diagnosis. The significance of these models is highlighted by their capacity to enhance the quality and amount of data, as well as to address the issue of data imbalance for rotating machinery fault diagnosis. Moreover, the existing research emphasizes challenges in this field. For instance, one notable challenge is model generalization. This refers to the issue where a model trained on a specific set of machinery may not exhibit satisfactory performance when applied to another set, owing to variations in operating conditions, machine types, and fault characteristics. Additionally, the utilization of generative models can be intricate and demand substantial computational resources for effective training. [PERSON] et al. [65] synthetized one-dimensional signals for augmenting the training set of diagnosis models, [PERSON] et al. [56] proposed latent optimized stable GAN (LOSGAN) for data augmentation, [PERSON] et al. [66] combined wavelet transform, GANs and CNNs for varying working conditions, [PERSON] and [PERSON] [67] boosted the training process by using improved GAN for imbalanced datasets, whereas [PERSON] et al. [59] employed GANs by transforming time signals into 2D images. Wasserstein GANs were investigated by [PERSON] et al. [68] and [PERSON] et al. [58], while [PERSON] et al. [69] included self-attention modules. Cycle-consistent adversarial learning and cycleGANs [70] were investigated by [PERSON] et al. [71], while [PERSON] et al. [72] employed cycleGANs to translate an undamaged domain to a damaged domain for structural health monitoring. CycleGANs for bearing fault diagnosis were also investigated by [PERSON] and [PERSON] [73]. The authors developed a cycleGAN model for converting data from healthy operating conditions to inner race damaged data for the well-known CWRU dataset [32]. In this instance, all the operating conditions are seen by the generative model, although only for undamaged data. At its core, existing research on GANs for rotating machinery frequently necessitates the presence of data from all the operating conditions to train generative algorithms that effectively augment or balance the training datasets. Furthermore, due to the extensive utilization of the CWRU experiments, distinct working conditions are frequently interpreted as different loads since speeds slightly vary in the CWRU signals. However, it has been recently noted by [PERSON] et al. [33] that the absence of wide-ranging RPMs could result in the lack of a meaningful change in working conditions. The primary objective of this study is to produce synthetic data that could replace the experimental damaged data required for training IFD algorithms in the field of industrial bearing diagnosis. Namely, this study suggests the generation of synthetic data also for operating conditions that have not been explicitly trained in the generative model.