Temporal Dependency Analysis in Predicting RUL of Aircraft Structures Using Recurrent Neural Networks
Abstract
The aviation industry is constantly striving for improved safety and operational efficiency, and one of the most important aspects of maintenance and reliability management is accurately predicting the remaining useful life (RUL) of aircraft structures. Parameters influencing RUL of aeroplane structures subjected to fatigue loading are investigated in this work. The manufacturing quality, usage profile, load spectrum, flight hours, environmental factors, maintenance history, upgrades, structural monitoring systems, operational procedures, and aging impacts are among the factors taken into account. Deep learning models are trained and validated using an extensive dataset that includes historical data on aircraft usage, maintenance records, and structural health monitoring. The temporal dependencies present in the data are captured using recurrent neural networks (RNNs) and other networks like LSTMs, which allow for the modeling of intricate relationships between the various components influencing RUL. To offer more accurate RUL predictions, the study highlights the importance of using both internal and external variables in a holistic approach to predictive modeling. The identification of systematic plan that affect structural integrity is made easier by the integration of advanced analytics and machine learning approaches.