Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Generative model-based clustering of directional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Display of information for time-critical decision making
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
An anomaly detection method for spacecraft using relevance vector learning
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Parameterless outlier detection in data streams
Proceedings of the 2009 ACM symposium on Applied Computing
A Multi-resolution Approach for Atypical Behaviour Mining
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
ACM Computing Surveys (CSUR)
Dynamic neural network-based fault diagnosis for attitude control subsystem of a satellite
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
UPBOT: a testbed for cyber-physical systems
CSET'10 Proceedings of the 3rd international conference on Cyber security experimentation and test
Atypicity detection in data streams: A self-adjusting approach
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
A puppet interface for retrieval of motion capture data
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
On the behavior of kernel mutual subspace method
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Two effective methods to detect anomalies in embedded systems
Microelectronics Journal
Sequential change-point detection based on direct density-ratio estimation
Statistical Analysis and Data Mining
Review: A review of novelty detection
Signal Processing
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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.