## 2 Literature Review
### Industry 4.0 Technologies That Changed the Maintenance Paradigm  
As we mentioned earlier, the objective behind I4.0 is networking, as if it were a collaborative community, to collect, exchange and analyze data through embedded computer-controlled feedback circuits looking for optimal solutions and predict future behaviors. In that regard, ICT has allowed people, machines, and other objects to connect, and it has been possible to build a highly flexible production model, called the Cyber-Physical Production System (CPPS), which consists of transforming production supported in physical processes into production sustained by the use of CPS [19; 23]. On the other hand, sensors play a fundamental role in collecting data from the physical world to cyberspace, functioning as actuators that transmit feedback information generated from the cyber level to the physical space again, the latter feedback being obtained through computational skills and data analysis and management [32]. Naturally, these systems are complemented with other features, such as IoT that enable data transmission or other short-range communication  
Figure 1: From Industry 1.0 to Industry 4.0 (Adapted from [PERSON] et al. [17]). technologies, such as near field communication (NFC) or radio frequency identification (RFID). It is, therefore, possible for physical devices to be connected to each other (e.g., NCF) and/or by networks (e.g., IoT), so that it is feasible to obtain information from sensors or processes throughout the plant and store it in cloud-based data centers [33]. In addition, data production has reached unprecedented levels in many domains and has not been restricted to industry [34]. In the industry domain, though, the development of Big Data analysis techniques and technologies aims to promote the interpretation of data (i.e., that the data makes sense) that is usually collected from a wide variety of heterogeneous sources (e.g., sensors, temperature, voltages), increasing its complexity. The concept of the Internet of Services (IoS), sometimes identified as an extension of the IoT, is also involved in the exchange of information, adding a service-focused view to production [10]. Therefore, it is evident that the technologies mentioned are not only changing the paradigm of the production process but are also impacting the entire supply chain [35], from transportation to logistics, through health, and even maintenance and diagnosis [36]. After the 2 nd industrial revolution, engineers and industrialists started working on developing methods to optimize production in the search for a balance between cost reduction and quality increase [37]. As a result of mass production, machines and their components began to experience greater wear and tear and, consequently, higher degradation, making its mitigation a growing concern in order to ensure continuous and flawless production [5]. Thereat, the concept of maintenance finds its centrality in the industrial world. Perhaps, the best-known typology is the run-to-failure (R2F) or, also known as, corrective maintenance (CM) [38]. This type of maintenance essentially consists of repairing a machine only when a particular failure is detected, which typically implies that this equipment will stop producing when it is being repaired. Its underlying advantage is that the machine or component will exceed the maximum period of use that the supplier has stipulated. However, there is no way to avoid the unplanned shutdown of the machine and the consequent interruption of production [4]. To avoid breakage due to maintenance stops, the time-based (TM) or preventive maintenance (PM) was introduced. In PM, time planning or process iterations are foreseen for carrying out inspections and maintenance according to a schedule [38]. PM is commonly identified as the best form of preventing failures [38], but there is no learning about the machine's degradation profile for future actions [33], as well as a long suspension time and high operating costs [4].