## 2 State of the Art
Additionally, continuous supervision and action-taking processes were not explicitly considered. The authors of [14] provided a proposal for a more integrative perspective between all the functions involved, including the operational and logistic dimensions. The authors sought to determine the expected benefit that the technological maturity endowed would provide for airline operations and developed suitable maintenance strategies that incorporate newly acquired insights. They proposed a discrete event simulation framework that uses established parameters to describe the performance of a condition-monitoring technology and subsequently develops a suitable prescriptive maintenance strategy. Although their focus was on combined effects, considering the mobility of assets and interventions, predictive components were considered without uncertainty, and no specific continuous assessment was provided. An interesting contribution was presented in [44], because although the authors' main focus was the prescriptive algorithm and its performance when describing the system-modeling step, they emphasized that data-driven algorithms can be used to estimate the probability or duration of failure or categorize systems based on the severity of their usage [44]. However, they noted that the accuracy of such models is dependent on the quality and availability of data and that knowledge of distinct failure modes and stress variables is required to use them successfully. In our opinion, this is a critical aspect that, when not properly addressed, will hinder the PsM approach. Another significant contribution was made by [45], who avoided the real-time optimization problem. Another interesting contribution related to PsM frameworks is [PERSON] and [PERSON] [46], wherein the authors propose a conceptual framework and show the complexity of system function relationships, starting from the parts, components, subsystems, and systems linked to specific functions and their interrelations. They emphasize how, in their case, a probabilistic perspective helps rank the associated risks of the components and guide the estimation of RUL [14,47]. This information was used to optimally plan the maintenance and requirements of Just-in-Time spares before they failed. The authors claimed that this approach accounts for downtime, demand for spares, lead time, and budget allocation. Although integrated strategies such as Structural Health Monitoring (SHM) are suggested, the authors recognize that in-service equipment performance can be monitored online and that the performance prediction process is the foundation of life cycle management (LCM) and a critical element of PdM techniques in terms of helping to determine the condition of in-service equipment [48]. Again, the impact of the accuracy of the prediction of its probabilistic parameters is recognized. They propose increasing future efforts to quantify these crucial parameters. Regarding the side of RL applied to PsM, it is worth mentioning a contribution wherein the authors propose that the sequential optimization process can help overcome the classical limitation known as the Markov property [49,50], which states that the future can be determined only from the present state, which encapsulates all the necessary information from the past [7]. The authors also relax the hypothesis of an accurate representation of the environment in which the agent acts by enabling noisy observations from different sensors, providing a belief regarding the status of the asset rather than its deterministic value. After a detailed analysis of the proposed PsM frameworks, both for mobile and grounded industrial assets, it becomes clear how online monitoring of variables linked to functional characteristics is a unanimously accepted approach, but no methods of increasing these approaches' reliability over time have been provided. Normally, these frameworks are considered operating blocks, always providing accurate signals, but some authors have recognized that this characteristic is a source of weakness.