## 2 Literature Review
### Condition-Based and Predictive Maintenance  
Advances in the automation and integration of mechatronics in industrial machines have enabled real-time information about their status and functioning, namely through sensors [39]. CBM is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing, and maintenance decision-making [40]. Thus, sensors have promoted predictive maintenance typologies, since CBM involves a prior assessment of the asset's condition, monitoring significant parameters for its operation. According to the Portuguese standard NP EN 13306:2007--Maintenance terminology, this monitoring can be made according to a predefined calendar or according to a certain number of units of use of the equipment, serving as a reference for future actions.  
In recent years, several techniques have been developed to monitor and control the desired parameters of the equipment, such as analysis of vibrations, temperatures, pressures, ultrasounds, currents, and voltages [41; 42; 43; 44; 45]. Predictive maintenance can be based on the continuous monitoring of certain parameters of a machine or the entire process so that its current condition is monitored in real-time and its future operational state can be predicted [4]. [PERSON] et al. [33] consider that PdM aims to monitor and analyze the evolution of the degradation state of a machine, being possible to identify, in advance, the need for maintenance interventions before the occurrence of malfunctions. Thus, maintenance actions are performed only when necessary in order to maintain the operational status of the equipment. [PERSON] et al. [46] state that this process is aided by predictive tools based on historical data and integrity factors. [PERSON] et al. [4] further point out that the introduction of this maintenance practice optimizes a compensation scenario, maximizing the life cycle of the machine's components while maximizing its uptime (Figure 3a).  
We also highlight the fact that there is no universal agreement for the definitions of CBM and PdM. In some cases, there are authors who identify the concepts as synonyms [47], although many others make a clear distinction between the two practices [39; 48], and most of them observe CBM as a short-term measure, while PdM allows taking into account the future conditions of the asset, in order to maximize its life cycle and its components (Figure 3b). [PERSON] et al. [4] also stress that a proper integration of predictive maintenance can lead to a significant reduction of operational costs, which is one of the main objectives of I4.0, including, for example: schedule of maintenance actions; reduction of unexpected stoppage; reduction of unavailability by stopping when it is really necessary (i.e., when the degradation state requires it); development of data history for each equipment; elimination of unnecessary replacement of components if the machine is operating satisfactorily, and so on. Although CBM and PdM have been studied in the context of I4.0 in recent years, few researchers have paid attention to this type of maintenance techniques in the automotive industry [49]. Nevertheless, a major issue in production planning of the automotive industry is related to the unexpected downtime, which is associated with aging equipment [50]. The adoption of CMB and PdM is fundamental in this industry because it allows detecting defects, to predict failures, thus increasing performance [51]. It is in this regard that the application of modeling and management tools, such as BPMN in the maintenance process becomes useful, as is its use in other industries [52].  
Figure 2: Types of maintenance.