Machine learning and Data Mining Techniques in Health Monitoring of Artificial Satellites Mission_Crimson Publishers

Machine learning and Data Mining Techniques in Health Monitoring of Artificial Satellites Mission by Ashraf Darwish in Crimson Publishers: Open Journal of Civil Engineering
The development of an intelligent satellite health monitoring system is an essential issue in aerospace engineering that determines the state of health and failure of satellites based on telemetry data. The recent development of data mining and machine learning techniques makes it possible to use satellite telemetry data and to extract integrated information for the production of advanced health monitoring system. The editorial paper can be used as a guideline for the design of an intelligent health monitoring solution based on remote data. Satellites are amongst today’s most complex technical systems, they fulfill their mission in a very harsh and special environment. So, it is practically impossible to completely eliminate the possibility of anomalies or faults. Determining the state of health of these systems using traditional know ledge-based techniques or situation-based diagnosis is becoming more difficult due to the diversity of external factors and the degradation of performance, such as the state of the satellite that will change for the design stage, especially long-lived satellites. Therefore, telemetry data mining techniques have been developed to address the health monitoring operations. By analyzing system operations data to characterize and detect unusual behavior or anomalies and analyzing the fault detection and the impact on the system. Then, followed by discovering the relationship between dependent and independent variables to predict the next event or behavior of the satellites that ensures the highest level of reliability and security.


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