Development of Methods for the Parallel Application of FIR
and Reconstruction Analysis Modeling Techniques at Different Levels of
Granularity
Introduction
This project deals with the decomposition of complex systems into
suitable sub-systems that can be described by a smaller number of variables.
To this end, techniques of both reconstruction analysis and FIR have been
employed.
The main problem in dealing with large-scale systems is information overflow.
Too many sensors provide too many measurement values that ought to be
looked at and considered all simultaneously. When an error occurs, the
system rarely reacts with a single alarm signal. The primary difficulty
in dealing with errors is to recognize their cause. It is difficult to
identify the true cause of an error in the flood of information being
provided by lots of different sensors simultaneously. Unfortunately
in most cases, the time is limited that the plant operator has for
reacting to the received alarms. If the timing restrictions cannot be
met, the plant must be shut down.
Two Ph.D. dissertations were written that relate to this general theme.
Álvaro de Albornoz researched in his
Ph.D. dissertation, how FIR
can be used for fault monitoring of large-scale industrial processes.
He clearly recognized the problem of information overflow, and he also
recognized that a watchdog monitor of a complex industrial plant must be
hierarchically structured. Yet, he performed the structuring and
decomposition of such a system manually.
Josep Maria Mirats revisited
this problem in his
Ph.D. dissertation, and developed algorithms
for the automatic decomposition of large-scale systems into suitable
sub-systems.
Most Important Publications
- de Albornoz, A. (1996),
Inductive Reasoning and Reconstruction Analysis: Two Complementary
Tools for Qualitative Fault Monitoring of Large-Scale Systems,
Ph.D. dissertation, Llenguatges i Sistemes Informàtics, Universitat Politècnica
de Catalunya, Barcelona, Spain.
- Mirats, J.M. (2001),
Qualitative Modeling of Complex Systems by Means of Fuzzy Inductive
Reasoning: Variable Selection and Search Space Reduction,
Ph.D. dissertation, Tecnologies Avançades de la Producció, Universitat Politècnica
de Catalunya, Barcelona, Spain.
- Mirats, J.M., F.E. Cellier, R.M. Huber, and S.J. Qin (2002),
On the Selection of Variables for Qualitative Modelling of
Dynamical Systems,
Intl. J. General Systems, 31(5), pp.435-467.
- Mirats, J.M., F.E. Cellier, and R.M. Huber (2002),
Variable Selection Procedures and Efficient Suboptimal Mask Search
Algorithms in Fuzzy Inductive Reasoning,
Intl. J. General Systems, 31(5), pp.469-498.
- Mirats, J.M., F.E. Cellier, and R.M. Huber (2004),
Reconstruction Analysis Based Algorithm to Decompose a
Complex System into Subsystems,
Intl. J. General Systems, 33(5), pp.527-551.
Sponsors
- Consejo Interministerial de Ciencia y Tecnología (CICYT)
- Consejo Nacional de Ciencia y Tecnología (CONACYT)
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Last modified: July 20, 2005 -- © François Cellier