## 3 Literature Review
### Visible and Invisible Problem Solving
This technology could enable manufacturers to get greater insight into the performance of their assets by clearly identifying trends and characteristics to solve invisible problems that have not yet been fully recognized [27]. The insight and evidence contained in the data also enables predictive analysis, which is a key benefit of AI [27]. Through exploration of complex relationships in the data, new knowledge is generated that may be iteratively improved in a sustainable and continuous manner to increase the efficiency of industrial systems [27]. There are three basic paradigms in ML as in Figure 6: supervised, unsupervised and reinforcement learning [45]. Supervised learning, the most widely used method [45], learns a function that maps an input to an output based on labelled training data. The most common supervised learning algorithms include neural networks, support vector machines, decision trees, logistic regression and naive bayes classifiers [23]. The output variables may be either categorical or continuous and are known as classification tasks and regression tasks, respectively. Examples in the industrial context outlined by [PERSON] et al. [23] include process failure detection (classification) and physical property prediction such as thickness or surface roughness from parts processed by a numerical control machine (regression). Unsupervised learning, on the other hand, does not receive any output information. Its goal is to build representations of the input that can be used for decision making, predicting future inputs, efficiently communicating the inputs to another machine, etc. [47]. [PERSON]. [47] suggests that unsupervised learning can be thought of as finding patterns in data that would otherwise be considered as unstructured noise. Classic examples include clustering and dimensionality reduction. Lastly, reinforcement learning differs by interacting with its environment and producing actions. These actions change the state of the environment that causes the algorithm to receive rewards or punishments. According to [PERSON]. [47], the goal of the algorithm is to maximize the rewards and minimize the punishments it receives over its lifetime. It is closely related to control theory in engineering. While ML is one of the key enablers to evolve a traditional manufacturing system up to the Industry 4.0 level [48], ML is not applicable to every industrial problem. [PERSON] et al. [15] recommends that systems with differing complexity and uncertainty levels should have different maintenance strategies, and by extension, are not in need of ML solutions. In the case of HVAC systems on industrial sites, there is low availability of facilities personnel to tend to these assets, often outnumbered by 20 AHUs to one technician [41]. Therefore, costly external consultancy is required to ascertain the cause of complex issues [49] and prioritize the list of issues that need to be resolved. For this reason, the ideal solution is a proactive, data-driven maintenance strategy. Data-driven methods perform well in large-scale and complex systems, but they require high quality and sufficient training data [26], which has not been a priority in the past. Transitioning to data-driven, proactive and energy efficient practices faces many challenges. In practical applications, these challenges are not being adequately addressed as many hi-tech facility managers admit to being insufficient in terms of their collection and analysis of HVAC data [10]. Visible problem solving is the key focus in industry, but the frequency of their occurrence may be reduced through invisible problem solving. The collaboration of traditionally different disciplines (engineering and computer science) \"is necessary to drive progress\" [24], proven by the increasing number of studies analyzing the integration of domain specific frameworks, methodologies, and concepts with data mining methodologies [37].