Modelling Satellite Expected Lifetime for ISRO's Space Missions with Machine Learning Approaches

Abstract

The Indian Space Research Organization (ISRO) has been a pioneer in space exploration since its establishment in 1969. Over the decades, it has developed numerous satellite missions that have significantly contributed to various fields, including communication, earth observation, and navigation. Historically, predicting the expected lifetime of satellites has relied on extensive empirical data and expert assessments. ISRO has continuously innovated, transitioning from traditional methods to more sophisticated approaches, integrating advances in technology and science. The objective of this research is to leverage machine learning techniques to model and predict the expected lifetime of satellites in ISRO's space missions. By utilizing historical data and advanced analytics, the goal is to enhance the accuracy of lifetime predictions, thereby improving mission planning, resource allocation, and operational efficiency. Before the advent of machine learning, traditional methods for predicting satellite lifetime typically involved empirical models based on historical performance data, physical failure models relying on component testing, and expert evaluations through qualitative assessments. These methods often lacked the ability to efficiently analyze large datasets and were limited in their predictive capabilities. The traditional methods for estimating satellite lifetime at ISRO are often time-consuming, reliant on expert knowledge, and limited by the availability of historical data, leading to inaccuracies in lifetime predictions and inefficient resource management during missions. The proposed system involves collecting extensive datasets from past ISRO satellite missions, including operational data, environmental factors, and component performance metrics. This data will be pre-processed and analyzed to identify patterns and correlations. A series of predictive models will be developed to estimate satellite lifetimes, incorporating various features such as launch conditions, operational anomalies, and degradation trends. The system will also include visualization tools to present insights and predictions in a user-friendly manner for mission planners.
Keywords: , ,, , , , Predictive Analytics, Data Pre-processing, Pattern Recognition, Correlation Analysis, Space Missions, Satellite Operations, Launch Conditions, Operational Anomalies, Degradation Trends, Environmental Factors, Component Performance Metrics.