Interval Constraint Satisfaction Research Articles

PASSIVE ROBUST FAULT DETECTION USING A FORWARD-BACKWARD TEST

The problem of passive robust model based fault detection of dynamic systems using interval observers has been mainly addressed checking if the measured output is inside the interval of possible estimated outputs obtained considering uncertainty on model parameters. This task could be computationally expensive. In this paper, a mixed approach consisting in determining a computationally cheaper inner approximation of the estimated output interval, based on considering only the vertices of parameter uncertainty region (forward test), is combined with a parameter consistency check when the real measured output falls outside the inner solution (backward check). The backward check is implemented using interval constraint satisfaction algorithms, which can perform efficiently in deciding if the measured output is consistent with the interval model. Finally, this approach will be tested on a gas turbine nozzle servosystem.

Global energy targets and optimal operating conditions for waste energy recovery in Bisphenol-A plant

The worldwide increase in energy demands and the uncertainty of future energy costs make energy conservation a major element in controlling operating costs of any chemical process. This paper presents new opportunities for energy saving inside Bisphenol-A (BPA) plant through the optimal selection of process operating temperatures that enhance plant’s energy recovery system. These optimal operational modifications are systematically selected from a set of allowable process operating conditions without enumeration using novel interval constraint satisfaction model-based software called TEM_icons TM. The paper shows that simple in-process modifications for the sake of energy utilities saving in an actual BPA industrial facility can result in more than 17% saving in hot utility consumption.

A GMDH NEURAL NETWORK BASED APPROACH TO PASSIVE ROBUST FAULT DETECTION USING A CONSTRAINTS SATISFACTION BACKWARD TEST

This paper focus on the problem of passive robust fault detection using nonlinear models that include parameter uncertainty. The non-linear model considered here is described by a Group Method of Data Handling Neural Network (GMDHNN). The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed checking if the measured behaviour is inside the region of possible behaviours following what will be called in the following a forward test. In this paper, a backward test based on checking if there exists a parameter in the uncertain parameter set that is consistent with the measured behaviour is introduced. This test is implemented using interval constraint satisfaction algorithms which can perform efficiently in deciding if the measured state is consistent with the GMDHNN model and its associated uncertainty. Finally, this approach is tested on the servoactuator proposed as a FDI benchmark in the European Project DAMADICS.

Spreadsheets based on interval constraint satisfaction

AbstractSpreadsheets are difficult to use in applications, where only incomplete or inexact data (e.g., intervals) are available-a typical situation in various design and planning tasks. It can be argued that this is due to two fundamental shortcomings of the computational paradigm underlying spreadsheets. First, the distinction between input and output cells has to be fixed before computations. Second, cells may have only exact values. As a result, spread-sheets support the user only with primitive iterative problem solving schemes based on trial-and-error methods. A constraint-based computational paradigm for next generation interval spreadsheets is presented. The scheme makes it possible to exploit incomplete/inexact data (intervals), and it can support problem solving in a top-down fashion. Current spreadsheets constitute a special case of the more general interval constraint spreadsheets proposed.

Constraint reasoning based on interval arithmetic: the tolerance propagation approach

Interval constraint satisfaction (interval labeling) systems have traditionally been based on local Waltz filtering techniques that cannot in general determine global solutions. In contrast, this paper documents a related technique, tolerance propagation (TP), that generalizes the idea of numerical exact value propagation into interval propagation. In TP, consistency techniques based on the topology of the constraint net can be combined with techniques of interval arithmetic in a new fruitful way. In particular, by TP it is possible to determine global solutions for interval constraint satisfaction problems with arbitrary accuracy and without losing all attractions of simple local computations.