Filtering Information in Complex Temporal Domains Stephen Bay Pat Langley Mei Wang Marker Daniel Shapiro Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, California http://www.isle.org/ {sbay,langley,mei,dgs}@isle.org

Monitoring the Space Station Power Grid Given: thousands of variables measured every ten seconds; Detect: any significant anomalies as soon as possible. SSUs (2): primary power supply Batteries (6): Current, Voltage (38 cells) Pressure, Temp DDCUs (6): secondary power demand, all loads

Model-Driven Anomaly Detection Our approach to filtering high-dimensional temporal data relies on five key ideas: 1. use models and schedules to predict quantitative values; 2. compare predictions and observations to detect anomalies; 3. provide graphical aids that depict functional modules; 4. support modeling at multiple levels of abstraction; 5. give users control over level of detail and thresholds. This means combining techniques from model-based reasoning, simulation languages, and human-computer interaction.

Project Accomplishments (through 2/2002) In our first twelve months on this research project, we have: formulated a set of generic research problems in monitoring; studied the structure and function of the Space Station power grid; examined the actual telemetry stream from this system; designed an approach to fault detection and event filtering; developed a process modeling language for numeric prediction; used this language to model power system at various levels of detail; developed a method to compare predicted and observed values; implemented a technique for displaying anomalies graphically; demonstrated fault detection and event filtering using these tools; developed a method that uses machine learning to improve models; used this approach to construct accurate models of battery behavior. The main limitation involves the need to manage model complexity.

Project Accomplishments (through 10/2002) In the most recent six months on the research project, we have: extended the modeling language to represent hierarchical models; constructed detailed models of batteries and solar wing array; extended the modeling environment to simulate hierarchical models; extended anomaly detection from variables to faulty processes; implemented hierarchical propagation and filtering of alerts; implemented hierarchical display of both models and alerts; implemented a color coding scheme to signify severity of alerts; implemented ability to replay telemetry data and graph values; developed componential and causal views of hierarchical models. This approach provides a principled way to manage model complexity.

Hierarchical Quantitative Process Models To represent knowledge about the power grid, we use a modeling formalism that describes a system in terms of: the physical subsystems from which it is composed; the quantitative variables associated with each system; a set of causal processes and their effects on variables; cast as either instantaneous or differential equations; with conditions on when each process will be active; This process modeling language borrows ideas from research in qualitative physics and model-based reasoning. But it adapts them to domains that involve numeric variables.

Partial Hierarchical Model of the Power Grid system PowerStore; components ba1, ba2, ba3; variables I, maxPower, Power, charging; measurables I, Power, charging; equalities charging = b1.charging = ba2.charging = ba3.charging; process distributePowerCharging; conditions charging > 0; equations ba1.Power = Power * ba1.maxPower / maxPower; ba2.Power = Power * ba2.maxPower / maxPower; ba3.Power = Power * ba3.maxPower / maxPower; process totalMaxPower; equations maxPower = ba1.maxPower + ba2.maxPower + ba3.maxPower; process totalCurrent; equations i = ba1.i + ba2.i + ba3.I;

Partial Hierarchical Model of the Power Grid system ba1; variables charging, maxPower, Power, Vcb, i, soc, Vt, Rs; measurables charging, Power, Vcb, i, soc, Vt, Rs; parameters Rp = 100, Rload = 2.6, Icharge = 12; process ChargeDischarge; equations d[soc,t,1] = 0.001 * (36.2 + 76.2 * soc) / Rp); process FullCharge; conditions soc < 0.96, charging > 0; equations maxPower = Icharge * (VCb + Icharge * Rs); process MaintainCharge; conditions soc > 1.0, charging > 0; equations maxPower = 1.1 * (36.2 + 76.2 * soc); process VtCharge; conditions charging > 0; equations Vt = Vcb + I * Rs;

Graphical Display of Model Structure

Quantitative Model-Based Monitoring process model schedule of power generation/usage simulation predicted values initial system conditions anomaly detection GUI with visual alerts observed values (telemetry)

Evaluation of the Approach To demonstrate our approach to model-based monitoring and get initial feedback on our interface design, we: selected parts of the power grid for our initial study; developed partial models at multiple levels of detail; used mutated models to generate “observed” values; ran the monitoring system on these data to detect anomalies; displayed detected faults in our graphical user interface. Our experience with these runs has suggested revisions to both the monitoring method and the user interface.

Related Work on Filtering and Monitoring Previous research on intelligent filtering and monitoring includes: plan monitoring - for military plans (e.g., Shapiro et al., 1985) - for robotic plans (e.g., Washington et al., 1999) fault detection - in manufacturing systems (e.g., GenSym) - in space operations (e.g., Config, CRANS) activity monitoring - for detecting fraud (e.g., Fawcett & Provost, 1997) - for detecting computer intrusion (e.g., Maloof, 1995) However, few of these efforts address issues in human-centered computing and information overload.

Plans for Future Research In future work on filtering temporal information, we plan to: develop even more extensive models of the power grid; run models and monitoring method on more telemetry data; augment and improve the interface to serve users better; evaluate the resulting system on human test subjects; predict possible future faults through forward simulation; develop methods for handling missing values in data; use telemetry data to further improve models via learning; combine monitoring method with interactive scheduling. These extensions will involve integrating ideas from model-based reasoning, HCI, machine learning, and intelligent simulation.

The Problem of Filtering Temporal Data Humans often encounter domains involving many variables that change rapidly over time. Lacking the ability to process all these data, they need aids that detect interesting events and filter out the rest. Given: a domain with thousands of continuous variables; Given: values for these variables as a function of time; Given: knowledge about the domain and the user’s goals; Find: events interesting to the user as they occur. Our research goal is to design, implement, and evaluate intelligent assistants for this task.

The Task of Power Grid Monitoring Staff at Mission Control monitor the state of the electrical power grid for the International Space Station. Clearly, they would benefit from computational aids that helped them detect anomalies in this complex system. Given: ~50,000 variables in the Space Station power grid; Given: observed values for these variables every ten seconds; Given: schedules for usage/generation and expected effects; Find: significant divergences from expected values. We have selected this domain as our main testbed for research on intelligent filtering assistants.

Partial Process Model of the Power Grid process PowerGeneration; variables TimeOnOrbit; conditions sin(TimeOnOrbit) < 0.5; equations Supply = 20000; process PowerDistribution; variables Demand, Supply; BatteryPower = Supply – Demand; process Charge; variables BatteryPower, Q; conditions BatteryPower >= 0; # The battery saturates at full charge d[Q, t, 1] = (100000 – Q) * (1 – e^(– BatteryPower / (100000 – Q)));

Graphical Display of Model Structure