Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype
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Abstract
In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the In- ductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investi- gate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were pre- viously unknown and not previously simulated will be studied in earnest, as well as apparent de- ficiencies or misapplications that result from us- ing the data-driven paradigm. Our conclusions indicate that robust detection performance of sim- ulated failures using IMS is not appreciably af- fected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.
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anomaly detection, deployed applications, physics of failure, space vehicles, applications: space, data driven methods, Data-driven detection methodologies, simulation
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