# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 2 Literature Review
Thus, the traditional maintenance policy and RUL prediction of manufacturing systems are restricted to the manufacturing components' performance dependence. In another way, based on the age prediction model that takes into account the dependence of components for product quality necessities, a predictive maintenance approach with the functional importance of components is proposed. In addition, there are two key models of PdM--data-driven and model-based approaches--and the predicted maintenance structure involves two parts: system age prediction and maintenance plan determination. Several approaches from the research on the topic of selective maintenance strategy for multi-component systems have been studied. A set of operational constraints in selective maintenance optimization is considered, taking into account system and maintenance characteristics and mission profile. Based on these criteria, a general procedure for selective maintenance optimization is developed [4]. The data-based approach shows superior performance compared to the model-based method since the latter requires prior information on the system degradation mechanism; otherwise, the data-based model uses real data to predict the age of the system without knowing the environment of the system degradation process [21]. Generally, a data-driven approach requires several processing methods to extract features from the collected data and to build the best model; such types of requirements involve manual data processing and analysis [22]. Data-driven approaches have the capability to autonomously acquire features that would otherwise demand considerable expertise, time, and experience. [PERSON] et al. [23] adopted an advanced fusion technique based on deep random forests to optimize the fault diagnosis performance for gearboxes. This enhancement was achieved by utilizing signals from both acoustic and vibration sensors. [PERSON] et al. [24] suggested an innovative approach involving a modified deep auto-encoder driven by multi-source parameters. This method was applied to analyze performance degradation parameters from simulation data of aero-engines at the system level and experimental run-to-failure bearing datasets at the component level. [PERSON] et al. [25] introduced an innovative maintenance policy, utilizing a data-driven PdM approach. The decision making for performing maintenance for joint optimization of production and predictive maintenance is determined by introducing a hybrid deep learning technique based on a combination of a long short-term memory (LSTM) and convolutional neural network (CNN) to improve the prediction accuracy of the remaining useful life [26], and by combining the (LSTM) and Markov decision process to provide specific maintenance strategies in different degradation stages of the system [27]. [PERSON] et al. [28] proposed a model aimed at predictively optimizing maintenance and production schedules using available data. This model generates crucial information empowering decision-makers to minimize production costs. Different ML techniques have been used by different researchers for building predictive maintenance models from maintenance data. Since the relationship between features is not linear, the techniques used were also nonlinear. The ANN and SVR (Support Vector Machine) are those that have been the most used. However, deep learning approaches have provided an interesting opportunity to develop advanced prediction methods for maintenance data [29]. These models can automatically learn features and extract information despite high levels of abstraction in system data. Recently, artificial neural networks have usually become used in prediction system age and machine failure detection [30; 31].