## 2 Related Work  
The maintenance systems and their future development in Industry have been studied by [PERSON] [20], who theoretically proposed how maintenance could be conceptualized and operationalized within the framework of digital manufacturing, detecting dominant themes such as education and maintenance training, fact-based maintenance planning, intelligent work procedures, systems-perspective maintenance planning, and more. On the other hand, [PERSON] [21], proposed a theoretical reference framework for Maintenance for the factory of the future within the Industry 4.0 environment, but it has not been tested in the real world and does not take into account the human being. [PERSON] [22] proposes a maintenance strategy for decision-making through the implementation of condition-based monitoring (CBM) in the manufacturing industry, including a cost-benefit analysis. In developed countries, these maintenance systems are already being applied with new business models, and according to the World Economic Forum [23], predictive maintenance actions provide various benefits such as 12% reductions in repair times, reduction in up to 70% in failures of some machines, and a reduction in maintenance costs of 30%, among other benefits that investment in applied research of intelligent manufacturing systems brings. According to [24] maintenance's potential to improve the organization's robustness and resilience still need investigation, requiring a search for the main research gaps in industrial maintenance systems. Like any industrial machine, modern technology needs to have a maintenance methodology that helps it optimize its useful life. A research gap analysis of the study of predictive maintenance at the system level is developed by [PERSON] and [PERSON] [25] pointing out the lack of incorporation of interactions between components as the main research gap, with two aspects: (a) Reliability modeling and analysis, which is performed using Failure Modes and Effects Analysis (FMEA), Failure Modes, Effects and Criticality Analysis (FMECA), fault trees (dynamic), Bayesian networks (dynamic), and networks stochastic Petri; and (b) the primary literature focused on complex assets, which often involves modeling the relationship between component detection and state and component-component interactions. The second research gap lies in the lack of attention given to maintenance effects. Maintenance sensors' future failure events and maintenance actions can alter the state oflatent degradation and its trajectory in trivial ways. Figure 1 shows the maintenance space state, within which are the different trajectories of degradation that a physical asset can suffer from the beginning of its life cycle until a failure occurs, either without preventive intervention or with preventive interventions, but reaching the failure boundary. A critical study of existing problems in manufacturing maintenance systems has been made by [PERSON] et al. [26] pointing out that the lack of synergy between humans and intelligent machines represents the principal gap. On the other hand, [PERSON] et al. [27] point out the importance of developing feedback mechanisms to improve decision-making algorithms. In another study, [PERSON] et al. [28] review data-based decision-making methods for Industry 4.0 maintenance applications, pointing out a research agenda with the use of enabling technologies, such as the use of Augmented Reality (AR) that serves as an interface with decision-making algorithms for maintenance applications, or the use of the Internet of Things (IoT) to eliminate uncertainty to avoid implementing inappropriate autonomous maintenance actions, as well as vertical and horizontal integration in industrial systems, or the use of cloud computing, big data, additive manufacturing, autonomous robots and simulation, which could all be embraced in a digital twin approach to predictive maintenance.