Miguel Ángel Sainz

Fault detection and isolation of the three-tank system using the modal interval analysis

Abstract Analytical redundancy is a widely used technique for fault detection. It consists of comparing the behaviour of a real system with a reference obtained by simulation of its model. The main problem is that there are always imprecisions and uncertainties which are not represented in the model so the behaviour of the real system and the behaviour of the model are not exactly the same. One way to represent these uncertainties in the model is using interval models. The results of the simulation of these types of models may be represented by envelopes. This paper proposes an approach to generate envelopes based on interval techniques of the modal interval analysis. As an example, this approach is used to detect and isolate faults in a physical system formed by three interconnected tanks.

Quantified Set Inversion Algorithm with Applications to Control

In this paper, a new algorithm based on Set Inversion techniques and Modal Interval Analysis is presented. This algorithm allows one to solve problems involving quantified constraints over the reals through the characterization of their solution sets. The presented methodology can be applied to a wide range of problems involving uncertain (non)linear systems. Finally, an advanced application is solved.

Controller Design Under Fuzzy Pole-Placement Specifications: An Interval Arithmetic Approach

This paper discusses fuzzy specifications for robust controller design, as a way to define different specification levels for different plants in a family and allow the control of performance degradation. Controller synthesis will be understood as mapping a fuzzy plant onto a desired fuzzy set of closed-loop specifications. In this context, a fuzzy plant is considered as a possibility distribution on a given plant space. In particular, pole placement in linear plants with fuzzy parametric uncertainty is discussed, although the basic idea is general and could be applied to other settings. In the case under consideration, the controller coefficients are the solution of a fuzzy linear system of equations with a particular semantics. Modal interval arithmetic is used to solve the system for each alpha-cut. The intersection of the solutions, if not empty, constitutes the solution to the robust control problem

Formal Solution to Systems of Interval Linear or Non-Linear Equations

This is the first of two papers which present the Modal Interval Analysis as a framework where the search and interpretation of formal solutions for a set of simultaneous interval linear or non-linear equations is started on, together with the interval estimations for sets of solutions of real-valued systems in which coefficients and right-hand sides belong to certain intervals. The main purpose of this first paper is to show that the modal intervals are a suitable tool to approach problems where logical reference appear. Thus, it is possible to give a logical meaning to general solutions of the system of equations, obtained, in the linear case, by means of an algorithm whose convergence conditions are studied.

Comparison of interval and Monte Carlo simulation for the prediction of postprandial glucose under uncertainty in type 1 diabetes mellitus

In this paper, the problem of tackling uncertainty in the prediction of postprandial blood glucose is analyzed. Two simulation approaches, Monte Carlo and interval models, are studied and compared. Interval simulation is carried out using modal interval analysis. Simulation of a glucoregulatory model with uncertainty in insulin sensitivities, glucose absorption and food intake is carried out using both methods. Interval simulation is superior in predicting all severe and mild hyper- and hypoglycemia episodes. Furthermore, much less computational time is required for interval simulation than for Monte Carlo simulation.

Fault Detection in a Pilot Plant Using Interval Models and Multiple Sliding Time Windows

Abstract Analytical redundancy is one of the techniques that can be used for Fault Detection. An important problem in this case is how the uncertainty associated to the systems and the measurements is taken into account. This paper proposes to consider them by means of interval models and interval measurements. The consistency between them is checked and a fault is detected when there is an inconsistency thus avoiding false alarms. The used technique is also based on Modal Interval Analysis which provides tools to compute interval extensions of real functions with the adequate semantics and saves much computational effort compared to other techniques based on global optimization algorithms. Time windows of different lengths are used in order to improve the Fault Detection results. This method is being applied to several real processes within the European project CHEM.

SQualTrack: A Tool for Robust Fault Detection

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 systems behavior, obtained from the measurements, and the models 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.

Improving the interval ray tracing of implicit surfaces

This paper presents a fast and reliable method to trim non-solution regions in an interval ray tracing process. The “trimming algorithm” uses interval analysis to perform rejection tests in a set of pixels simultaneously, instead of individual pixels at each time. With this approach, the presented algorithm runs faster than the traditional interval ray tracing algorithm. Also, an interval algorithm to remove aliasing in the rendering of implicit surfaces is introduced. This algorithm obtains better visualizations than the traditional point sampling. This algorithm can render thin features that would be impossible to obtain with point sampling algorithms.

Interval Estimations of Solution Sets to Real-Valued Systems of Linear or Non-Linear Equations

This is a second paper devoted to present the Modal Interval Analysis as a framework where the search of formal solutions for a set of simultaneous interval linear or non-linear equations is started on, together with the interval estimations for sets of solutions of real-valued systems in which coefficients and right-hand sides belong to certain intervals. The main purpose of this second paper is to show that the modal intervals are a suitable tool to approach problems where logical references appear, for example, to find interval estimates of a special class of generalized sets of solutions of real-valued linear and non-linear systems, the UE-solution sets.

Quantified set inversion with applications to control

This paper describes a new reliable method, based on modal interval analysis (MIA) and set inversion (SI) techniques, for the characterization of solution sets defined by quantified constraints satisfaction problems (QCSP) over continuous domains. The presented methodology, called quantified set inversion (QSI), can be used over a wide range of engineering problems involving uncertain nonlinear models. Finally, an application on parameter identification is presented