#### Literature Review  
The interest in signal processing methodologies that enable the manipulation of data and information of the systems under monitoring and/or control is a reality in several fields of investigation [24; 25; 26; 27]. All these studies make available valuable knowledge which is used as the inspiration for applied studies such as the monitoring of HVAC systems' operation. Research on automated FDD in HVAC systems has been an active research field for more than 20 years. Being a system that contains numerous and simultaneously interacting sub-systems, the HVAC equipment can be considered complex and hard to model using physical or mathematical representations. In contrast to that, data-driven approaches are less affected by the complexity of the system, as they rely solely on historical operation data. Currently, data-driven methodologies are considered the most popular choice in the field, thanks to their reduced development complexity and the fast-growing of machine learning powered methods [28]. Like in other machinery classification problems, many data-driven techniques have been applied to HVAC FDD. Classical approaches such as Support Vector Machines (SVM) [29], Bayesian Networks [30] and Artificial Neural Networks (ANN) [31] are popular choices in the field. Advanced methodologies such as ensemble learning techniques [32] and deep learning algorithms [33; 34] are also gaining attention in the last few years. Hence, the state of the art proves the viability of applying data-driven schemes for the supervision of HVAC systems in general, and AHU in particular; where classical machine learning approaches such as ANN already show a great performance for the operation classification tasks. However, FDD models are data intensive and as HVAC systems mostly operate under fairly normal conditions; therefore, the facility manager may need to either (i) wait for a long time to collect enough fault data--even if they can be correctly labelled--to train a useful FDD algorithm, or (ii) rely on established industry standards and exhaustively simulate potential scenarios. Neither option takes advantage of the new digitalization paradigm, in which a rich stream of data from well-equipped buildings and different HVAC will be available to overcome the problem of needing enough representative data of the system operation. In the last few years, some strategies have already been proposed to deal with the imbalanced dataset problem arising from the lack of faulty data, falling the majority of them in the semi-supervised learning field. In [35], [PERSON] et al. developed several classical machine learning methodologies in a semi-supervised manner for AHU FDD, based on the idea of pseudo labelling those samples with high-class confidence and adding them to the training pool, hence improving the models' performance. In addition, [PERSON] et al. [36] follow a similar approach using ANN as their machine learning approach. In [37; 38], the authors make use of generative adversarial networks (GAN) as a pre-processing stage to address the imbalanced data problem in an AHU and a chiller respectively, by generating a stream of synthetic data with the same distribution as the original dataset. All these proposals, however, are built upon the hypothesis that a scarce number of samples is sufficient to characterize the underlying physical behaviour of the machinery, which is something difficult without falling into an overfitted response in any machine supervision field, especially in HVAC related applications, that are heavily affected by the seasonality. In this regard, transfer learning aims to provide a more generalizable solution approach [39] to address not only the challenge of having highly imbalanced data but also the problem of having a fully transferable model applicable to buildings without requiring nearly any historical sample.