Mahsa Babaee - Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Iran
Jafar Gheidar-Kheljani - Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Iran
Mostafa Khazaee - Faculty of Aerospace, Malek Ashtar University of Technology, Iran
Mahdi Karbasian - Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Iran

In many important industries, such as aerial transportation, offshore wind turbine (OWT) structures, and nuclear power plants that reached or are near the end of their useful life, the structural conditions for continued usage are acceptable. Thus, safe continued operation with required modifications and assessment is more cost-effective than replacing them with a new system. To achieve this goal, many studies have been performed on predicting failure time and remaining useful life, especially in systems that require a very high level of reliability. The present review investigates the articles that predict the remaining useful life or failure time in aviation systems, from three perspectives: ۱. Methods and algorithms, especially Machine Learning algorithms, which are growing in recent years in the field of Prognosis and Health Management. ۲. Historical predictors such as working life history, environmental conditions, mechanical loads, failure records, asset age, maintenance information, or sensor variables and indicators that can be continuously controlled in each system, such as noise, temperature, vibration, and pressure.۳. Challenges of researches on prediction of the failure time of flying systems. The literature assessment in this field shows that using diagnostic and prognostic outputs to identify possible defects and their origin, checking the system's health, and predicting the remaining useful life (RUL) is increasing due to market needs.

Aviation accidents, failure time, Machine Learning, Prognosis and health management, Remaining useful life