Process Control for Risk Identification and Anomaly Detection

Recently, the Department of Homeland Security streamlined or created Information Sharing & Analysis Centers (ISAC) in order to minimize our nations’ critical infrastructure systems’ vulnerability to a dehabilitating terrorist attack. The fourteen ISACs monitor threats to our food and water supply, our nation’s economic backbone, and to core industrial infrastructure networks like telecom and energy supply. Currently, more ISACs are being developed while the DHS encourages more and more industry participants. In short, ISACs are a major priority of the DHS. As the number of centers and corporate and public partners increase, the amount analysis grows exponentially.

Lambert and Sarda proposed this influx of data be monitored in real-time by employing statistical process control. Instead of the traditional venue for SPC, a manufacturing line, they modeled an infrastructure network as a system of components and proposed one could measure the interactions of these components over time. Similarly to SPC for manufacturing, their paper argued a monitor could set upper and lower thresholds for acceptable interaction rates within the monitored system. When those thresholds are exceeded in a manufacturing environment, the process stops and the offending cause is pinpointed and fixed. A similar breach of these thresholds in our infrastructure SPC could pinpoint and alert authorities of a potential terrorist threat or abnormal event.

When setting up SPC in the manufacturing environment, choosing your process variables is a critical design aspect. This paper’s focus extends Sarda’s process variables proposals, interaction entropy and interaction informativity, while proposing new process variables that measure indirect interactions between system components. These process variables are then clarified by analyzing several annual reports of disturbances, load reductions, and unusual occurrences on the bulk electric systems of the electric utilities in North America. We plot the process variables against this database with a software tool we explicitly developed for this purpose.

Risk identification, scenario analysis, statistical process control, failure modes and effects analysis, information entropy, systems engineering, anomalies detection

20  Visual inspection in industrial manufacturing

Maybe some manufacturing leads. – B

9  An unsupervised anomaly detection patterns learning algorithm

1  A formal framework for positive and negative detection schemes

Kaplan, Stanley; Garrick, B.J.

Society for Risk Analysis, Volume:1 Issue: 1, 1981

Pages 11-27

Our analysis begins by dissecting an event log for a complex system.

measures :

1. direct interactions/week

2. indirect interactions/week

3. direct/indirect

4. % of system components affected

5. toggle switch – when does scenario enter into “Red” zone of heightened danger

6. how does that toggle switch our analysis

from my presentation:

1. We need to address a series of questions that focus on the what are scale or universe of analysis is.

• 
  Have I considered using a HHM to focus the analysis to a smaller subsection of possible events?
  
• 
  What is the universe of components? Start with a complete system and work your way backwards.
  

3. Get familiar with 6 sigma analysis from Pr. Haimes’ book (in the extreme event analysis chapter)

Acknowledgements