## 2 Background and Related Research  
According to a survey of hi-tech manufacturers in the UK, 40% are stuck in reactive mode for their HVAC maintenance strategy [10]. It appears this is caused by a lack of information on the operation of these systems, as 62% of facility managers in this survey admit they are deficient in relation to the collection and analysis of their HVAC data [10]. However, in the literature, a myriad of data-driven approaches have been developed and there is an increasing trend [18]. The disparity between academia and practical applications appears to be caused by a difference in the data. According to [PERSON] et al. [18], most studies in the literature utilize experimental or simulated data from laboratory tests such as ASHRAE RP-1312 [19] or those made available by the Lawrence Berkeley National Laboratory [20]. While these datasets address a severe lack of publicly available benchmark datasets, they are not representative of real-world datasets. In 2022, [PERSON] et al. [21] found that the performance of FDD strategies developed on simulated data and directly applied to real building data is less that satisfactory. In practical applications, the granularity of the data is less sufficient, the metadata is not well described, and there is a lack of well-labelled faults with multiple severity ratings. Therefore, many of the studies in the literature do not address the challenges that are faced in practical applications of data-driven analysis. To summarize the data issues identified in the literature, Table 1 categorizes the main data-related challenges outlined by systematic reviews of data-driven applications in both generic industrial and HVAC-specific studies. The table is by no means exhaustive but does provide an indication that many challenges are pervasive in both fields. To classify the challenges outlined in Table 1, the data issue classification proposed in the Industrial AI paradigm is adopted [27]. Professor [PERSON] [27] coined the term \"Industrial AI\" to describe the application of artificial intelligence in the industrial domain. Data issues are classified as broken, bad quality and background, and are collectively known as the \"3B\" [27]. Firstly, data may be considered broken if it does not fulfil the requirements of the analysis. Professor [PERSON] [27] outlines that \"data can't just be numerous--it must be comprehensive\". As in Table 1, data availability is a common issue in the literature, whereby the data is described as missing, incomplete or insufficient. Data may be unavailable for reasons such as lack of sensors, faults in communication protocols or damaged sensor networks. In the case where the necessary information is available, it must accurately represent the physical phenomena it measures. Bad quality data does not reach the standard required to apply data-driven techniques and is often considered as noisy. Noisy data is composed of issues such as incorrect data, improper data, duplicated data and inconsistent data, which is often caused by sensor faults. The different fault types are visualized in Figure 1 and are commonly classified as drift fault (a), bias fault (b), precision degradation fault (c), spike fault (d) and stuck fault (e) [28]. The presence of a sensor fault may lead a data-driven algorithm to incorrectly classify the anomaly as a component fault, which would lead to a lack of trust in these methods by industrial practitioners. Distinguishing between sensor faults and component faults is a key challenge in HVAC applications [18], which requires an understanding of the operation of the equipment to overcome. This is a key component of the background issue that relates to the need for understanding the context of the data. In practical applications, the data is often inadequately labelled, which means the interpretation of domain experts is needed to clarify the meaning of each measurement [22].