This paper proposes a novel "knowledge-free" anomaly detection method for spacecraft based on regression techniques. This method learns a linear or nonlinear probabilistic regression model in the learning phase by applying a regression technique to a massive telemetry data of spacecraft, and then monitors the real-time telemetry data using the constructed model. This approach can be seen as adaptive limit-checking because it sequentially predicts proper envelop of a target time-series based on

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... he past data of itself and other relevant series. 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 and from actual space operation of Engineering Test Satellite VII (ETS-VII). ( 313 )