TY - JOUR ID - 157761 TI - Prediction of Failure Time and Remaining Useful Life in Aviation Systems: Predictors, models, and challenges JO - International Journal of Reliability, Risk and Safety: Theory and Application JA - IJRRS LA - en SN - AU - Babaee, Mahsa AU - Gheidar-Kheljani, Jafar AU - Khazaee, Mostafa AU - Karbasian, Mahdi AD - Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Iran AD - Faculty of Aerospace, Malek Ashtar University of Technology, Iran Y1 - 2021 PY - 2021 VL - 4 IS - 2 SP - 97 EP - 106 KW - Aviation accidents KW - failure time KW - Machine Learning KW - Prognosis and health management KW - Remaining useful life DO - 10.30699/IJRRS.4.2.11 N2 - 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: 1. Methods and algorithms, especially Machine Learning algorithms, which are growing in recent years in the field of Prognosis and Health Management. 2. 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.3. 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. UR - http://www.ijrrs.com/article_157761.html L1 - http://www.ijrrs.com/article_157761_8ac7773da3982f084b90e5626e8e402c.pdf ER -