## 1 Introduction  
The impact of machine learning (ML) and deep learning (DL) approaches is growing in the domain of rotating machinery diagnosis [1; 2; 3; 4]. The increasing interest in these methodologies can be attributed to the potential of these algorithms to acquire knowledge in a highly automated manner, enabling them to establish meaningful connections between input variables, frequently derived from condition monitoring signals [5], and diagnostic outputs. This correlation is achieved with a remarkable level of accuracy, making these techniques valuable for predictive maintenance objectives. In this context, the involvement and expertise of operators in maintenance decision making are diminished, since they are increasingly dependent on the knowledge acquired by intelligent algorithms through the analysis of training data. This procedure is frequently associated with the examination of rolling element bearings (REBs) [6; 7; 8; 9; 10], as they serve as mechanical components that can gather informative data pertaining to the entire machinery and its overall performance [11; 12; 13; 14]. Intelligent Fault Diagnosis (IFD) [1] has emerged as a prominent field in the past decade, focusing on the utilization of Artificial Intelligence (AI) for diagnosing machine components. Numerous approaches have been employed in this context. In the field of ML, the extraction of diagnostic features is typically performed manually, such as in the application of algorithms like support vector machine (SVM) [15; 16; 17] or k-nearestneighbors (kNN) [18; 19; 20]. Then, DL is distinguished by the adoption of an automated approach for extracting features [21; 22], which might manifest through the application of deep networks like convolutional neural networks (CNNs) [23; 24; 25; 26; 27; 28]. The latter frequently engage with multi-dimensional data such as images. In the scenario of diagnosing rotating machinery, these images are commonly depicted as time-frequency images, such as spectrograms or Continuous Wavelet Transform (CWT) representations [27; 29]. However, the efficacy of ML and DL approaches relies on the availability of a sufficient amount of training data [3; 30; 31] that faithfully capture the potential operating conditions and machine failures. The availability of data is often limited in industrial applications. This limitation is particularly relevant when attempting to capture a comprehensive and representative distribution of potential damages. The currently accessible public datasets available in the field of bearing fault diagnosis [32; 33; 34; 35; 36; 37] have been derived from extensive experimental efforts. However, they do not provide the same magnitude of data as certain datasets found in other domains, such as image recognition [38], sound detection [39] and natural language processing [40; 41] for which foundation models are available [42]. In those domains, AI has demonstrated significant potential, and the presence of labeled data is certainly a contributing factor to this outcome. Due to this rationale, a significant portion of the scholarly literature concerning IFD focuses on the implementation of approaches with the objective of diminishing the substantial amount of training data. Transfer Learning (TL) [3; 43; 44; 45] approaches are employed to transfer previously gained knowledge from ML models across different tasks and domains representing different diagnostic scenarios. This approach effectively reduces the requirement for extensive training data. In general, TL often entails the process of building a model on a large dataset and subsequently employing it as a pre-trained basis for addressing similar or related problems. This technique offers the advantage of requiring less data and training time compared to training models from scratch. In the literature concerning machine fault diagnosis, there have been reports of instances where diagnostic capabilities have been successfully transferred to new working conditions of the same machine [46; 47], as well as to different machines [48].