# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 6 Conclusions  
This paper proposed a predictive model for selective maintenance decision making using machine learning models and mathematical programming. After conducting tests and comparing various machine learning methods, we observed that the ANN, SVR with linear kernel function and SVR with RBF Kernel methods stand out for their reliability in terms of prediction accuracy for selective maintenance. In this paper, these three machine learning models were compared on the provided dataset, and the ANN yielded the highest prediction score. Moreover, at the end of each mission, the constructed model predicts the real age of each component according to the system data collected during the previous operations. Then, multi-level maintenance actions can be performed in order to improve the actual age system and avoid equipment failure, while minimizing the total maintenance cost under time and reliability constraints. In this study, we considered that the selective maintenance strategies depend on the actual conditions of each component, i.e., a low actual age due to degradation phenomena and operating conditions requires a costlier maintenance strategy that strongly improves the age and compensates the degradation effect. To illustrate the robustness of the proposed strategy, a historical maintenance data of the FMS real study was explored to determine a selective maintenance plan. The obtained results show that the proposed model can be used in several industrial fields and help decision-makers determine the best maintenance policy to perform during each intermission break which guarantees the completion of the next operation with a high system reliability. For the prediction model, future work can focus on a more complex configuration system taking into account the factors of time and environmental conditions (temperature, pressure, humidity, etc.) and implement more ideas using artificial neural networks and machine learning techniques to estimate the probability of the system failure. Subsequently, integrate this probability assessment into the optimization problem and maintenance planning. Also, it is an interesting perspective to integrate other learning methods such as CMFIRL (completely mode-free integral reinforcement learning) [45] and policy iteration (PI) algorithm, by using the technique of neural network linear differential inclusion (LDI) [46] with our joint production and maintenance problem. Figure 7: Evolution of system reliability after performing maintenance actions. **Author Contributions:** Writing--original draft, A.A.; Writing--review & editing, M.A.K. ; Supervision, Z.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data confidentiality. **Conflicts of Interest:** The authors declare no conflict of interes. **Notations**  
The following notations: