###### Abstract
On the one hand, some researchers tackle this issue by constructing detailed simulation models where all the operating points can be studied, but as [PERSON] and [PERSON] mentioned in [12], these simulations end up being highly complex due to the following reasons: (i) HVAC systems consist of highly interrelated components that may present complex coupling effects (e.g., a fan degradation may affect the supply airflow and/or increase fan power, but it also may affect the heat transfer performance of coils and its energy consumption), (ii) operational faults can present diverse impacts on different aspects of the building performance depending on the seasonal period when it occurs, (iii) a particular fault may present different operational characteristics and needs to be simulated accordingly (i.e., static vs abrupt vs degradation faults), (iv) the results may vary depending on the empirical curves or the component model employed, and (v) fault modelling should take into account various modes of operation. On the other hand, some authors suggest recreating all faults on the real equipment and using the newly generated samples to train the model. However, in real applications, obtaining data able to characterize the system is also a difficult task due to two main reasons. First, it is generally hard to obtain a full training dataset with the same distribution as the online operating conditions because of seasonal variations, building envelope, and occupancy variations that affect the system operation and control. Obtaining it would imply a large data collection phase, including sparse samples of a whole year before the model can be considered robust and reliable enough. Second, in-service HVAC machines are not allowed to work continuously under faulty conditions, since it involves not only high economic costs but also comfort issues. In addition, when the system experiences a fault, hardly ever it is correctly labelled in the building energy management system. Therefore, most of the studies end up using synthetic data or limited sets of representative acquisitions, leading to non-generalizable solutions with an intrinsic risk of overfitting that can only be applied to specific equipment. As pointed out by [PERSON] and [PERSON] in [13], a data-driven FDD framework based on taking advantage of several buildings' data streams will contribute effectively to palliate the effects of a limited set of data. Leveraging information coming from different buildings is still a challenging task, and at the same time, it is expected to be a revolution to promote the massive deployment of FDD systems in the sector [14]. This paradigm shift over classical data-driven approaches is known as transfer learning, and it is defined as the knowledge transfer among similar applications [15] using the knowledge learnt from previous tasks and domains to a new one. Transfer learning has been successfully applied to other machinery or condition monitoring fields such as bearings in rotating machinery [16] or nuclear power plants monitoring [17]. In the last few years, several works have been published in the field of HVAC applications, especially those related to chillers [18; 19]. Nonetheless, they are usually applied to synthetic datasets [20] that are a replica of the real installation or to data collected in a laboratory [19] where the source and target machines share external conditions and several components making their problem more similar than in actual industrial and real applications. Few researchers have addressed the issue of applying transfer learning methodologies to highly dissimilar datasets, and it remains unclear how these approaches will respond when pushed to their limits to build a common feature space for the studied domains [21]. Studying the domain and data samples to use during model training has been proven to help in increasing the model's performance by reducing the negative transfer of knowledge caused by highly dissimilar samples [22].