# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 1 Introduction  
System reliability and performance are considered one of the central interests in Industry 4.0, given the huge economic losses due to unexpected machine breakdowns. In this case, several maintenance strategies are adopted to avoid these failures and to maintain a system in working order such as preventive, corrective, predictive maintenance activities, and reliability. These strategies aim to ensure a given level of system reliability that allows it to complete the remaining operations with maximum availability [1; 2; 3]. Moreover, most multi-component systems in industrial applications execute consecutive missions with planned breaks. During these breaks, several maintenance actions can be performed on the various components to improve their lifetime. However, maintaining all components seems very expensive and sometimes unnecessary; thus, selective maintenance aims to determine the optimal number of components and the type of maintenance actions to be performed on the system to successfully achieve the next operation [4]. Such a system may contain manufacturing equipment, aircraft, computer systems, power generation systems, etc. Industrial equipment can operate through the week and be maintained during the weekends or on breaks, the use of the aircraft can be planned, and its maintenance activities between flights and military equipment can be applied to its maintenance actions between missions. In these different cases, maintenance actions should be selected to achieve the desired level of system reliability. At certain maintenance time points, there could be some possibility to plan maintenance actions during the break, e.g., minimal repair, \"as bad as old\"; different preventive maintenance actions, \"a certain level of imperfect maintenance\"; or machine overhaul, \"as good as new\". After maintenance action, the system should operate with the desired reliability level over the next mission until the next programmed break [5]. Since each of the available maintenance options expands somemaintenance means (time, budget, availability of repairman, etc. ), it is necessary to allocate resources optimally. In addition, the selective maintenance strategy has been widely studied in the literature; most existing research has focused on a model where the system degradation follows a stochastic process. However, these proposed maintenance selective strategies are mainly based on theoretical studies and estimated parameters [6]. Furthermore, even if a theoretical or analytical model is created, it is not easy to apply directly in industrial practice with multiple operational variables that exist and can impact the validity of the model over time. To overcome these limitations, artificial intelligence (AI) and machine learning (ML) methods have recently been implemented in several industrial environments to address challenges encountered in model-based approaches. One of the applications of AI is the integration of ML and maintenance, in which companies rely on new types of maintenance for their machines. Such methodology uses predictive tools to estimate when maintenance is necessary. It continuously monitors machine conditions over time and allows for early detection of failures based on historical data. This maintenance strategy is known as predictive maintenance [7]. Therefore, it is possible to collect enormous amounts of real data generated from the various system components, and then learn pertinent information models from these data to use the unhidden designs for decision making in selecting the necessary components that need to be maintained and performing an automatic fault detection to minimize the downtime and related costs and increase the use the components rate [8]. However, in some classical ML models, feature engineering is a necessary technique used to manually manipulate data and create new features to find the best model representing the problem.