Joaquim Armengol Llobet | Universitat de Girona (original) (raw)

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Papers by Joaquim Armengol Llobet

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An impor... more One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the system's behavior, obtained from the measurements, and the model's behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is SemiQUALitative TRACKing (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project Advanced Decision Support System for Chemical/Petrochemical Manufacturing Processes and are also described in this paper.

Els models matematics quantitatius son simplificacions de la realitat i per tant el comportament ... more Els models matematics quantitatius son simplificacions de la realitat i per tant el comportament obtingut per simulacio daquests models difereix dels reals. Lus de models quantitatius complexes no es una solucio perque en la majoria dels casos hi ha alguna incertesa en el sistema real que no pot ser representada amb aquests models. Una forma de representar aquesta incertesa es mitjancant models qualitatius o semiqualitatius. Un model daquest tipus de fet representa un conjunt de models. La simulacio del comportament de models quantitatius genera una trajectoria en el temps per a cada variable de sortida. Aquest no pot ser el resultat de la simulacio dun conjunt de models. Una forma de representar el comportament en aquest cas es mitjancant envolupants. Lenvolupant exacta es complete, es a dir, inclou tots els possibles comportaments del model, i correcta, es a dir, tots els punts dins de lenvolupant pertanyen a la sortida de, com a minim, una instancia del model. La generacio duna e...

IFAC Proceedings Volumes

Abstract In this paper, interval arithmetic is introduced to study plants with nonlinear parametr... more Abstract In this paper, interval arithmetic is introduced to study plants with nonlinear parametric perturbations. Common robustness problems as stability or performance analysis are reduced to the evaluation of interval functions. A new methodology of handling interval functions is introduced and applied to robustness analysis. Fast zero inclusion/exclusion algorithms are introduced in order to be applied into robust control design procedures.

5th IFAC Symposium on Robust Control Design (2006), 2006

Abstract Robust parameter identification consists of finding approximations for the set of parame... more Abstract Robust parameter identification consists of finding approximations for the set of parameters of a given model that are consistent with the input/output observations. When interval uncertainties are considered, consistency can be defined in different ways by applying universal or existential quantifiers to the intervals representing input, output and model parameters, leading to different physical interpretations. In this paper a new approach to robust parameter identification based on modal intervals is introduced. Several consistency tests are stated as first order logical ...

Lecture Notes in Mathematics, 2014

Report Internatl IIiA, 1998

Abstract. Imprecision and uncertainty in systems can often be expressed with interval models. Sim... more Abstract. Imprecision and uncertainty in systems can often be expressed with interval models. Simulation of these models, known as semiqualitative simulation, produces envelopes. These envelopes can be characterised by several properties such as completeness, soundness; they can be overbounded or underbounded. Simulation of such interval models can be performed by several means including quantitative, qualitative and semiqualitative simulators. A brief description of the different types of simulators is ...

PARA’04 Workshop on State-of-Art in Scientific Computing, 2004

12th International Workshop on Principles of Diagnosis DX, Mar 7, 2001

Interval models may be used in many cases to express the imprecision and the uncertainty related ... more Interval models may be used in many cases to express the imprecision and the uncertainty related to complex systems. The envelopes may be used to represent the results of the simulation of these models. One of the applications of the envelopes is as reference behaviour for Fault Detection (FD) based on analytical redundancy. In this case, the properties of the envelopes (completeness, soundness) have important consequences on the results of the FD, like missed or false alarms. This paper presents the Modal Interval ...

Workshop on Applications of Interval Analysis to Systems and Control MISC'99, 1999

Abstract. An interval model can express the imprecision and the uncertainty associated to the mod... more Abstract. An interval model can express the imprecision and the uncertainty associated to the modeling of a system. The result of the simulation of one of these models can be represented in the form of envelope trajectories. These envelopes can be characterized by several properties such as completeness or soundness, that lead to the concepts of overbounded and underbounded envelopes. The simulation of such interval models can be performed by several means including qualitative, semiqualitative and quantitative ...

Notconsidered intheanalytical modeloftheplant, uncertainties always dramatically decrease theperf... more Notconsidered intheanalytical modeloftheplant, uncertainties always dramatically decrease theperformance of thefault detection taskinthepractice. Tocopebetter withthis prevalent problem, inthispaperwe develop a methodology using ModalInterval Analysis whichtakes intoaccount those uncertainties intheplantmodel.A fault detection method isdeveloped basedonthismodelwhichisquiterobust to uncertainty andresults innofalse alarm. Assoonasafault isdetected, anANFISmodelistrained inonline tocapture themajorbehavior oftheoccurred fault whichcanbeused forfault accommodation. Thesimulation results understand- ablydemonstrate thecapability oftheproposed methodfor accomplishing bothtasks appropriately. I.INTRODUCTION

Tdx, Feb 18, 2008

... A way to represent this uncertainty is by using qualitative or semiqualitative models. A mode... more ... A way to represent this uncertainty is by using qualitative or semiqualitative models. A model of this kind represents a set of models indeed. ... The exact envelope is complete, ie includes all the possible behaviours of the model, and sound, ie every point ...

This paper tries to show the way neural networks and fuzzy logic could be applied to control non ... more This paper tries to show the way neural networks and fuzzy logic could be applied to control non linear systems. This is interesting to teaching. Non linear systems are good examples to apply several control techniques on them, from classical control to artificial intelligence. The capacities of the Neural Networks to learn non linear behaviour, and the Fuzzy systems to extrapolate the results, are here under test and comparison. Topologies and parameters have been tested, to differentiate possibilities and benefits from the both techniques. In this particular example, the goal is to control a process at different set-points with minimum error.

Lecture Notes in Mathematics, 2013