###### Abstract  
Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3-5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided. maintenance scheduling; predictive maintenance; preventive maintenance; prognostics; remaining useful life; reliability +
Footnote †: journal: _Future Internet_  
0000-0001-933-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000  
2. Technical and economical limitations in developing RUL models: It is not feasible to develop RUL models for every component in an industry due to technical constraints and complexities. Furthermore, it may not be economically advantageous to invest in monitoring systems for all of the parts of industrial equipment. 3. Consideration of additional factors: Maintenance planning should not solely focus on RUL. Other factors, such as the availability of resources (experts, tools), need to be taken into account for effective maintenance planning. 4. Accounting for uncertainty in RUL estimation: RUL estimation involves inherent uncertainty, i.e., there is not a perfect RUL estimation model that can make predictions with 100% accuracy. The errors in RUL estimation should be considered when planning maintenance actions, mainly to avoid the overestimation of the RUL near the point of failure, which may have a negative impact since a critical failure may occur. These limitations highlight the need for a comprehensive approach to maintenance planning that goes beyond RUL prediction and that considers production planning [8] and technical limitations. In response to these challenges, this study presents a novel framework aimed at optimizing maintenance procedures in different scenarios. Building upon a previous work developed by [9], which introduced an RUL prognostic method based on the mixture of generalized fault trees (GFTs) and a k-fold ensemble of long short-term memory networks (k-LSTM), designated k-LSTM-GFT, this research proposes an innovative policy based on predictive maintenance. This exploits the failure probability estimated by the k-LSTM-GFT model to account for a safety margin for the RUL prediction, i.e., when the failure probability increases, the safety margin also increases, to reduce the risk of overestimating the RUL and at the same time exploit the useful life of industrial settings. Furthermore, recognizing that RUL methods may not always be feasible due to the lack of monitoring systems, we introduce an adaptive dynamic preventive maintenance (PMD) policy that dynamically calculates the time between interventions to account for changes in the distribution of the time between failures due to the increasing degradation of some parts or due to changes in working conditions. The proposed framework is validated in two different use cases.