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 opera...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Ryohei Fujimaki, Takehisa Yairi, Kazuo Machida
• Keywords: Advanced Anomaly Detection, Conventional Anomaly Detection, Data Mining, KDD 2005, Telemetry Data