Monitoring of complex continuous physical systems has been traditionally accomplished in computer-based process control software by one or both of the following methods: 1) establishing limit checks for sensors and raising an alarm whenever a sensor's value crosses one of these thresholds, and 2) comparing predicted values from a simulation to actual sensor values and flagging discrepancies. These anomaly detection techniques are not as robust as they need to be. Failures can manifest in ways which are not captured by these traditional methods. Furthermore, some anomalous behaviors are more naturally detected at the level of global interactions affecting multiple sensors. We describe extensions to the traditional techniques for anomaly detection, as well as new anomaly detection techniques based on alternate models of what distinguishes normal from abnormal behavior. Some of these techniques are designed to capture anomalies at individual sensors; some detect anomalies across collections of sensors. To assist in reasoning about complex global behaviors, we construct and simulate a causal model of the physical system being monitored. These techniques have been tested on data from the Environmental Control and Life Support System (ECLSS) of Space Station Freedom (SSF) and are being applied in advanced monitoring prototypes for the SSF External Active Thermal Control System (EATCS) of SSF and the Environmental Emergency and Consumables Management (EECOM) subsystem of the Space Shuttle.
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