Guiding Principles of UNB's PAWS Research Premise: Incorporating wireless sensor networks (WSNs) in an industrial process-control setting promises to substantially increase the number of process variables that can be monitored and controlled, due to the enhanced ability to add, upgrade and/or reconfigure process variable wireless sensors and actuators. Objective: Extract value from the potentially large quantity of raw data from a distributed oil and/or gas production facility that is served by a WSN, by developing and fielding an intelligent supervisory system with software agents to: Reconcile process data: processing raw data to detect and remove outliers, correct inconsistencies (e.g., material and energy imbalance) and reduce the effects of error sources (e.g., noise); this is distinctly more powerful than standard filtering approaches. Identify linearized models of the process, for process understanding and use by other agents; the best algorithms are encapsulated to form a general, self-diagnosing agent. Detect and isolate sensor and actuator faults, and, for faulty sensors, accommodate the fault; the FDIA agent is automated and state-of-the-art. Coordinate with the WSN to provide optimal energy management without destabilizing the process control loops; a novel approach is implemented in this agent. Optimize process operations; this idea/sub-objective is not yet addressed. Goals: to demonstrate these capabilities on serious/realistic process models, and, ultimately, to implement the ICAM agents as function blocks (under standard IEC 61499) for use in industrial process control applications. UNB's and CBU's R&D is being integrated by (1) item 4 above, and (2) installing ICAM agents in the WINTeR Testbed (see ICAM/WINTeR Integration). Visit the UNB PAWS web site for more detailed technical information. Information supplied by: Jim Taylor Last update: 23 February 2010 Email requests for further information to: Jim Taylor (jtaylor@unb.ca)