Tree-based Nonparametric Prediction of Normal Sensor Measurement Range Using Temporal Information

Currently, limit-checking on telemetry sensor data of a spacecraft is widely used to detect its faults and anomalous behavior. Since classical limit-checking usually considers only a priori fixed pair of upper and lower bounds for each sensor variable, it sometimes fails to detect phenomena that are anomalous only in certain operating modes. To handle this problem, we present a method to predict normal ranges of sensor measurements adaptively based on status variables of telemetry data and temporal information. In the proposed method, a regression tree is learned using status variables, and each data point is labeled according to the terminal node of the tree it reached. Three new temporal features are generated from the sequence of the label, and a quantile regression forest is learned using both status variables and the generated features. Normal ranges are calculated from approximate distribution predicted using the quantile regression forest. We apply this method to actual telemetry data with simulated anomalies, and confirmed that the proposed method can detect temporal anomalies with a lower false alarm rate than the previous method.