# Classification of Systems and Maintenance Models
## 2 Literature Review
A study [44] proposed a technique for managing the maintenance of wind turbines using artificial intelligence approaches to reduce the overall cost. According to the study, the electrical system, gearbox, generator, and blades account for more than 80% of the risk factor and related downtime; as a result, they should be monitored and inspected more frequently than the others. The study in [45] created a big data analytics platform that lowers maintenance costs by optimizing the maintenance schedule through CBM optimization and increasing the accuracy of quantifying the RUL prediction uncertainty. The study [46] proposed a wind farm predictive maintenance approach that considered the economic dependency among subassemblies and component-level major and minor repairs. A simulation method was developed to assess maintenance costs. The study in [47] proposed a reinforcement learning method to explore optimal predictive maintenance policies that optimize production and maintenance costs. The study in [48] determined an optimal preventive maintenance schedule that was predicted based on the industry's maximum availability of critical part manufacturing systems. A study [49] developed a highly accurate RUL prediction for machinery using sensor-monitoring data. The proposed approach provides a more accurate RUL prediction than existing data-driven prediction methods. The study in [50] built a distributed system with artificial intelligence assistance for applications in manufacturing plant-wide predictive maintenance based on sensors. The study in [51] considered a sensor deployment problem to minimize maintenance costs. The authors developed a maintenance cost model for IoT networks that considers thermal degradation and battery depletion. A study [52] proposed a maintenance model for protection systems with imperfect inspections. The conditional probabilities of correct and incorrect inspections are assumed to be constant. The maintenance effectiveness indicator was the expected cost per regeneration cycle. Studies [53,54] have shown that the probabilities of imperfect inspections at condition-based and predictive maintenance are functions of the degradation model parameters and strongly depend on time. In the study [55], the case of imperfect testing of the crack depth in a fighter wing was considered. The study showed that flight hours have a significant impact on the probabilities of a false positive and a false negative. The study in [56] analyzed six main components of intelligent predictive maintenance: (1) sensor and data acquisition, (2) signal pre-processing and feature extraction, (3) maintenance decision-making, (4) key performance indicators, (5) maintenance scheduling optimization, and (6) feedback control and compensation. The study [57] presented a new data-driven predictive maintenance approach that covers the full process, from using RUL prediction to making maintenance decisions. The maintenance cost per unit time was calculated. The study in [58] proposed probabilistic models for assessing mission success probability, system survival probability, expected number of inspections during the mission, and total estimated losses for a system subject to imperfect inspections. In the developed models, the probabilities of false positives and false negatives were time-independent. The studies [59; 60; 61; 62; 63; 64] considered the application of preventive, condition-based, and predictive maintenance techniques in the context of Industry 4.0. Among the novel maintenance approaches and techniques, special attention is given to the data-driven approach, conventional machine learning approaches, and machine prognostics for RUL estimation. Studies [65; 66; 67; 68; 69; 70] introduced prescriptive maintenance techniques that may not only predict the condition of a machine in the future but also suggest proactively timed decisions for certain maintenance activities such as inspection, repair, and replacement.