1,356 research outputs found

    Automated Fault-Detection for Small Satellite Pointing Control Systems Using One-Sided Learning

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    In this paper, we propose a ground-based automated novelty detection system for a small satellite attitude dynamics control system using a one-sided learning algorithm: One-Class Support Vector Machine (OC-SVM) method. This fault-detection system was designed to only learn from nominal behavior of the satellite during the commissioning phase and to identify and detect anomalies when there was a subtle behavioral failure in the attitude control system. The detection system was trained by only observing the nominal attitude dynamics behavior of a small satellite for a period of time. Training data was obtained from reaction wheel outputs in a healthy attitude control system, and reaction wheel currents and angular velocities were selected as training features. A one-class classifier was built from a hyperplane decision function during training. An adaptive Sequential Minimal Optimization (SMO) method was utilized to solve the quadratic problem in the application of OC-SVM algorithm to provide an optimal solution for the hyperplane decision function. Two tests were performed on the system to validate its feasibility and detection accuracy. Untrained reaction wheel bearing failures were added into the attitude control system validation tests to examine whether the fault-detection system was capable of detecting and diagnosing the reaction wheel failures. Training and testing performance for the fault-detection system are presented with discussion

    FIESTA: An operational decision aid for space network fault isolation

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    The Fault Tolerance Expert System for Tracking and Data Relay Satellite System (TDRSS) Applications (FIESTA) is a fault detection and fault diagnosis expert system being developed as a decision aid to support operations in the Network Control Center (NCC) for NASA's Space Network. The operational objectives which influenced FIESTA development are presented and an overview of the architecture used to achieve these goals are provided. The approach to the knowledge engineering effort and the methodology employed are also presented and illustrated with examples drawn from the FIESTA domain

    Predicting software faults in large space systems using machine learning techniques

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    Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers) could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates

    A Data-Driven Approach to Cubesat Health Monitoring

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    Spacecraft health monitoring is essential to ensure that a spacecraft is operating properly and has no anomalies that could jeopardize its mission. Many of the current methods of monitoring system health are difficult to use as the complexity of spacecraft increase, and are in many cases impractical on CubeSat satellites which have strict size and resource limitations. To overcome these problems, new data-driven techniques such as Inductive Monitoring System (IMS), use data mining and machine learning on archived system telemetry to create models that characterize nominal system behavior. The models that IMS creates are in the form of clusters that capture the relationship between a set of sensors in time series data. Each of these clusters define a nominal operating state of the satellite and the range of sensor values that represent it. These characterizations can then be autonomously compared against real-time telemetry on-board the spacecraft to determine if the spacecraft is operating nominally. This thesis presents an adaption of IMS to create a spacecraft health monitoring system for CubeSat missions developed by the PolySat lab. This system is integrated into PolySat\u27s flight software and provides real time health monitoring of the spacecraft during its mission. Any anomalies detected are reported and further analysis can be done to determine the cause. The system can also be used for the analysis of archived events. The IMS algorithms used by the system were validated, and ground testing was done to determine the performance, reliability, and accuracy of the system. The system was successful in the detection and identification of known anomalies in archived flight telemetry from the IPEX mission. In addition, real-time monitoring performed on the satellite yielded great results that give us confidence in the use of this system in all future missions
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