Sebastián Tornil

ROBUST FAULT DETECTION USING INTERVAL CONSTRAINTS SATISFACTION AND SET COMPUTATIONS 1

Abstract In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and noises is addressed. Fault detection is formulated as a set-membership estimation problem being this one the contributions of the paper. A state estimator that describes the set of all the states consistent with modelling uncertainty, measured data and noise bounds is presented. Two possible implementations of such state estimation, based on constraint satisfaction and set computation techniques, of the state estimator are proposed and compared, being this a second contribution. Finally, the proposed approaches are applied to detect faults in limnimeters of a piece of Barcelona sewer network.

Robust Fault Detection: Active Versus Passive Approaches

Abstract The problem of robustness in fault detection has been treated using basically two kinds of approaches: actives and passives. Most of the literature in robust fault detection is focused on the problem of active approach. The passive approach is based mainly in the idea of using adaptive thresholds. In this paper the passive approach based on adaptive thresholds produced using an model with uncertain parameters bounded in intervals, also known as “interval models” will be compared with active approaches based on decoupling the effect of unknown parameters using a robust active approach based on parity equations. The paper will also analyse the different effects that affect the residual, i.e., disturbances, noises and model errors. At the end of the paper an example comparing the active approach based on parity equation and a passive approach based on an adaptive threshold produced using an interval model will be presented.

Fault Detection Using Interval Models

Abstract In this paper, two of the problems that appear in the use of interval models for fault detection in systems with uncertain but bounded physical parameters are shown: the increment of uncertainty during the identification procedure and the possibility of hidden faults even when the real uncertainty is used. Two alternatives in the use of these models for fault detection are presented: interval prediction and interval simulation. Finally, using a simple example, the effect of the problems in both alternatives is studied in order to compare them.

Robust fault detection using inverse images of interval functions

Abstract In this paper, a new robust fault detection test based on calculating the inverse image of an interval function is presented. It is based on a consistency test which relies on tools from interval analysis such as set inversion or contractors to check if there exists a member in the family of models described with an interval model that can explain the measured data. The proposed test is compared to related approaches of fault detection using interval analysis. The main features of the new test will be extracted and advantages and drawbacks discussed. An example will be used to illustrate the behaviour.

Integration of dynamic models in gas turbine supervision control

This paper presents the use of dynamic models in a real time supervisory system for fault detection in gas turbines. The analytical redundancy, given by quantitative and qualitative models of different gas turbine subsystems, is implemented in a specific software Ca/spl sim/En, which is very useful to represent hard to measure and ill-known dynamic systems, such as the case of gas turbines.

Robust Fault Detection Using Interval Constraints Satisfaction and Set Computations1

: In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and noises is addressed. Fault detection is formulated as a set-membership estimation problem being this one the contributions of the paper. A state estimator that describes the set of all the states consistent with modelling uncertainty, measured data and noise bounds is presented. Two possible implementations of such state estimation, based on constraint satisfaction and set computation techniques, of the state estimator are proposed and compared, being this a second contribution. Finally, the proposed approaches are applied to detect faults in limnimeters of a piece of Barcelona sewer network. Copyright

Robust Fault Detection Using Inverse Images of Interval Functions

: In this paper, a new robust fault detection test based on calculating the inverse image of an interval function is presented. It is based on a consistency test which relies on tools from interval analysis such as set inversion or contractors to check if there exists a member in the family of models described with an interval model that can explain the measured data. The proposed test is compared to related approaches of fault detection using interval analysis. The main features of the new test will be extracted and advantages and drawbacks discussed. An example will be used to illustrate the behaviour.

Simulink Blockset for Fault Detection Using Interval Models

Abstract This paper describes a tool to aid in the analysis and design of fault detection systems for processes which present bounded structured uncertainty (only bounds for the values of their physical parameters are known). This uncertainty is considered in the fault detection methodology by using interval models to represent the processes. The tool has been implemented using the well-known Matlab/Simulink CACSD framework. This framework is very useful in the analysis and design of control systems and can also be used in the analysis and design of supervisory systems (which must include fault detection capabilities).

Time-Domain Robust Stability Test Under Plant and Controller Interval Uncertainty

Abstract Once a controller has been designed for a certain plant with any method available, one would like to know if that controller will perform well despite structured uncertainty in the plant. Furthermore, as controller implementation is concerned, one is also interested in its fragility (Keel and Bhattacharyya, 1997). In this paper, interval parametric uncertainty is considered. As an alternative to testing robust stability by polynomial methods (Bhattacharyya et al , 1995; Ackermann, 1993), one can perform a robust simulation of the time domain response of the closed loop taking into account plant and controller uncertainties. It is shown that, based on this simulation, it is possible to test the closed loop robust stability.