document: An approach to spacecraft anomaly detection problem using kernel feature space Development of advanced anomaly detection and failure diagnosis technologies for spacecraft is a quite significant issue in the space industry , because the space environment is harsh , distant and uncertain . While several modern approaches based on qualitative reasoning , expert systems , and probabilistic reasoning have been developed recently for this purpose , any of them has a common difficulty in obtaining accurate and complete a priori knowledge on the space systems from human experts . A reasonable alternative to this conventional anomaly detection method is to reuse a vast amount of telemetry data which is multi-dimensional time-series continuously produced from a number of system components in the spacecraft . This paper proposes a novel `` knowledge-free '' anomaly detection method for spacecraft based on Kernel Feature Space and directional distribution , which constructs a system behavior model from the past normal telemetry data from a set of telemetry data in normal operation and monitors the current system status by checking incoming data with the model . In this method , we regard anomaly phenomena as unexpected changes of causal associations in the spacecraft system , and hypothesize that the significant causal associations inside the system will appear in the form of principal component directions in a high-dimensional non-linear feature space which is constructed by a kernel function and a set of data . We have confirmed the effectiveness of the proposed anomaly detection method by applying it to the telemetry data obtained from a simulator of an orbital transfer vehicle designed to make a rendezvous maneuver with the International Space Station .
doc_bio_tags: O O O B B I O O B I I O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O B O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O