ENHANCING ISRO’S MISSION PLANNING WITH AI-DRIVEN SATELLITE LIFETIME PREDICTION MODELS

Since its inception in 1969, the Indian Space Research Organisation (ISRO) has been at the forefront of space exploration, contributing significantly through a wide range of satellite missions in areas such as communication, Earth observation, and navigation.Traditionally, the prediction of satellite lifetimes has relied on empirical models, component testing, and expert judgment.While effective to a certain extent, these conventional approaches often struggle with analyzing large volumes of data and are limited in predictive accuracy.To address these challenges, this research proposes the application of machine learning techniques to model and predict the expected lifetime of ISRO satellites.By leveraging historical mission data and advanced analytical methods, the aim is to enhance the precision of lifetime estimations, ultimately supporting better mission planning, resource management, and operational decision-making.The methodology involves collecting comprehensive datasets from past satellite missions, including operational parameters, environmental influences, and component performance records.This data will undergo preprocessing to identify significant patterns and correlations.Using this processed information, a suite of machine learning models will be developed to estimate satellite lifespans based on variables such as launch conditions, operational anomalies, and degradation behaviors.Additionally, visualization tools will be incorporated to provide actionable insights in an intuitive format, enabling more informed planning by mission teams.