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Stephen Bay Pat Langley Mei Wang Marker Daniel Shapiro Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, California

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Presentation on theme: "Stephen Bay Pat Langley Mei Wang Marker Daniel Shapiro Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, California"— Presentation transcript:

1 Stephen Bay Pat Langley Mei Wang Marker Daniel Shapiro Institute for the Study of Learning and Expertise 2164 Staunton Court, Palo Alto, California Filtering Information in Complex Temporal Domains

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

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

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

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

6 To represent knowledge about the power grid, we use a modeling formalism that describes a system in terms of: Hierarchical Quantitative Process Models 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. the physical subsystems from which it is composed; the physical subsystems from which it is composed; the quantitative variables associated with each system; the quantitative variables associated with each system; a set of causal processes and their effects on variables; a set of causal processes and their effects on variables; cast as either instantaneous or differential equations; cast as either instantaneous or differential equations; with conditions on when each process will be active; with conditions on when each process will be active;

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

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

9 Graphical Display of Model Structure

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

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

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

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

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

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

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

18 Graphical Display of Model Structure


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