The role of artificial intelligence in fault-tolerant process-control systems

This paper discusses potential applications of Artificial Intelligence in real-time process control systems from the perspective of fault tolerance. We emphasize two areas of fault tolerance, namely, fault recovery and reconfiguration. For fault recovery, we present telescopic replication technique using a frame based knowledge representation method for facilitating real-time recovery. The impact of knowledge representation on fault recovery is addressed. For reconfiguration, we discuss how to generate contingency plans to cope with unexpected situations via planning and learning by using a lattice structure. The application of these techniques to a chemical batch process-control system is shown.

[1]  M.P. Georgeff,et al.  Procedural knowledge , 1986, Proceedings of the IEEE.

[2]  Victor R. Lesser,et al.  The Application of Artificial Intelligence Techniques to Cooperative Distributed Processing , 1979, IJCAI.

[3]  P. M. Melliar-Smith,et al.  Formal Specification and Mechanical Verification of SIFT: A Fault-Tolerant Flight Control System , 1982, IEEE Transactions on Computers.

[4]  Andrew S. Cromarty,et al.  "Fast" Is Not "Real-Time": Designing Effective Real-Time AI Systems , 1985, Other Conferences.

[5]  William R. Nelson,et al.  REACTOR: An Expert System for Diagnosis and Treatment of Nuclear Reactor Accidents , 1982, AAAI.

[6]  D. C. Chen.  An expert planner for the dynamic flight environment , 1985 .

[7]  Richard Fikes.  Knowledge Representation in Automatic Planning Systems , 1977 .

[8]  Hiroshi Motoda,et al.  A Diagnosis Method of Dynamic System Using the Knowledge on System Description , 1983, IJCAI.

[9]  William L. Luyben,et al.  Process Modeling, Simulation and Control for Chemical Engineers , 1973 .

[10]  Theodore J. Williams.  The Development of Reliabililty in Industrial Control Systems , 1984, IEEE Micro.

[11]  Farokh B. Bastani,et al.  Telescopic replication in hierarchically structured process-control programs , 1989, [1989] Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences. Volume II: Software Track.

[12]  D. M. Smith,et al.  Interfacing An Intelligent Decision-Maker To A Real-Time Control System , 1984, Other Conferences.

[13]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[14]  J. Guillermard,et al.  pms-1 - A real-time advisor for the boron control in PWR nuclear reactors , 1986, Robotics.

[15]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .