## 2 Literature Overview
### Predictive Maintenance  
Predictive maintenance is one of the most important topics in Industry 4.0 and industrial production. Its purpose is to constantly monitor the process in real time and so to detect tendencies that can lead to failures in advance to have them fixed before they can cause production manufacturing and/or financial problems. Importance and well-earned popularity of predictive maintenance is documented by many studies as the one written by [PERSON] et al. (2020), which monitors the raising trend of predictive maintenance (as a part of computer science) at the expense of engineering as the former dominant expertise in the field ([PERSON] et al., 2020). Predictive maintenance usually deals with production industry companies and their processes. [PERSON], [PERSON] and [PERSON] focus on this topic in their work (2019) and mark the possibility of solving a problem in its initial phases, before the problems cumulate, while the original one is still solvable with relative ease as the biggest benefit of predictive maintenance. The reduction of planned service time is also seen as a big advantage. Traditional systems are often unable to receive information about approaching failures, leading to repetition of certain errors. Other, similar machines and parts of system can be affected, too. All of those lead to unnecessary cost increases. The authors of the study use Machine Learning and Big Data algorithms to decide what data to use, determine where and how it should be collected, and uncover the causes of failures ([PERSON] et al., 2020). Another solution in the field was proposed by [PERSON], [PERSON], [PERSON] et al., who, in their paper (2018), introduce integrated self-tuning device for predictive maintenance,based on Big Data technologies, evolved in accordance with Industry 4.0 for the needs of production industry companies. One of its devices is virtual unlimited horizontal scalability, support of Big Data platforms and self-tuning approach which chooses the most suitable prediction algorithm [44]. [PERSON] and [PERSON] (2018) focus on unexpected situations (hard to predict), such as unwanted degradation of the tool or shock damage, in the context of predictive maintenance. They present an overview of condition-based predictive maintenance solutions aimed to avoid these unplanned production failures. They conclude that in the future, there will be a need for multi-phased maintenance model [45]. [PERSON] et al. (2020), compare 562 studies directly or loosely related to predictive maintenance; out of those, they closely focus on 38, the ones mostly connected with the main subject. The numbers are a further proof of how the fields in Industry 4.0 are interconnected. The study concludes by confirming the importance of predictive maintenance but warns about several challenges that are still to be investigated, as the whole field is relatively new [46]. Other research focuses more closely on a specific problem. As an example, let us discuss the paper by [PERSON] and [PERSON] (2021), describing predictive maintenance for multi-component systems of repairables in aircraft maintenance. The integrated approach presented in the paper opens with Remaining-Useful-Life prognostics. It is demonstrated on a fleet of aircraft, each equipped by a cooling system. The results show the integrated approach reduces the costs of maintenance by 48% relative to a corrective maintenance strategy and by 30% relative to a preventive maintenance strategy. Another advantage of the approach is replacing components in anticipation of failure, thus not wasting their useful life [47]. Connection of predictive maintenance to IoT is described in a paper by [PERSON] and [PERSON] (2021). They propose an approach for production lines in manufacturing, where data generated by IoT sensors in real time are used to detect signals for potential failure in advance to their presumable occurrence. The system uses machine learning methods for this.