Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1081870.1081917 | KDD |
Keywords | Field | DocType |
current system status,expert system,past normal telemetry data,spacecraft anomaly detection problem,conventional anomaly detection method,advanced anomaly detection,kernel feature space,incoming data,proposed anomaly detection method,anomaly phenomenon,anomaly detection method,telemetry data,spacecraft,normal operator,behavior modeling,international space station,feature space,principal component analysis,principal component,kernel function,probabilistic reasoning,qualitative reasoning,time series data,a priori knowledge,anomaly detection,time series | Space industry,Kernel (linear algebra),Data mining,Anomaly detection,Orbital maneuver,Space environment,Computer science,Artificial intelligence,Machine learning,Kernel (statistics),Qualitative reasoning,Spacecraft | Conference |
ISBN | Citations | PageRank |
1-59593-135-X | 54 | 2.22 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryohei Fujimaki | 1 | 193 | 16.93 |
| Takehisa Yairi | 2 | 294 | 29.82 |
| Kazuo Machida | 3 | 109 | 22.02 |