Set-membership parity space approach for fault detection in linear uncertain dynamic systems

Special Issue: Set-Membership Methods Applied to FDI and FTC.In this paper, a set-membership parity space approach for linear uncertain dynamic systems is proposed. First, a set of parity relations derived from the parity space approach is obtained by means of a transformation derived from the system characteristic polynomial. As a result of this transformation, parity relations can be expressed in regressor form. On the one hand, this facilitates the parameter estimation of those relations using a zonotopic set-membership algorithm. On the other hand, fault detection is then based on checking, at every sample time, the non-existence of a parameter value in the parameter uncertainty set such that the model is consistent with all the system measurements. The proposed approach is applied to two examples: a first illustrative case study based on a two-tank system and a more realistic case study based on the wind turbine fault detection and isolation benchmark in order to evaluate its effectiveness.This work has been partially funded by the grant CICYT SHERECS DPI-2011-26243 of Spanish Ministry of Education and by the European contract i-Sense (ref FP7-ICT-2009-6-270428)Peer Reviewe

Redundancy relations and robust failure detection

All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. The problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection is addressed. A significant amount of intuition concerning the geometry of robust failure detection is provided

Models of Financial System Fragility

This survey analyses two types of models: 1. Models based on assumptions of monetary and financial market equilibrium disturbance, in line with mainstream thinking according to which if there is a self-regulating market the units would have rational expectations, and the crisis would be a temporary phenomenon caused by exogenous shocks. Here are the main objectives and features characteristic of three generations of models; 2. Models based on financial instability hypothesis, taking into account the dynamics of financial market, as well as the role of uncertainty, interdependency and dynamic complexity. We present here Minsky’s concept of financial instability and then analyse the content of some simplified models.instability, model generations, balance sheet, hedge units, speculative units, Ponzi units, cyclical fluctuations, complexity

Redundancy relations and robust failure detection

Bibliography: p. 18-19."January, 1984""N00014-77-C-0224" "NGL-22-009-124"Edward Y. Chow, Xi-Cheng Lou, George C. Verghese, Alan S. Willsky

Final report on the development of methodologies for the detection of system failures, for the design of fault-tolerant control systems, and for the analysis of systems containing switches and subject to abrupt changes.

"March 1984."Bbliography: leaves 14-15.ONR Grant N00014-77-C-022

Fault detection and diagnosis of a plastic film extrusion process

This paper presents a new approach to the design of a model-based fault detection and diagnosis system for application to a plastic film extrusion process. The design constructs a residual generator via parity relations. A multi-objective optimisation problem must be solved in order for the residual to be sensitive to faults but insensitive to disturbances and modelling errors. In this paper, we exploit a genetic algorithm for solving this multi-objective optimisation problem and the resulting fault detection and diagnosis system is applied to a first-principles model of a plastic film extrusion process. Simulation results demonstrate that various types of faults can be detected and diagnosed successfully

Approximating fault detection linear interval observers using -order interval predictors

"This is the peer reviewed version of the following article:Meseguer, J., Puig, V., and Escobet, T. (2017) Approximating fault detection linear interval observers using ¿-order interval predictors. Int. J. Adapt. Control Signal Process., 31: 1040–1060., which has been published in final form at https://doi.org/10.1002/acs.2746. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."Interval observers can be described by an autoregressive-moving-average model while ¿-order interval predictors by a moving-average model. Because an autoregressive-moving-average (ARMA) model can be approximated by a moving-average model, this allows establishing the equivalence between interval observers and interval predictors. This paper deals with the fault detection application and focuses on the equivalence between the ¿-orderintervalpredictorsand the interval observers from the point of view of the fault detection performance. The paper also proves that it is possible to obtain an equivalent ¿ - order interval predictor for a given interval observer with the same fault detection properties by the appropriate selection of the ¿ - order. A condition for selecting the minimal order that provides the ¿ - order interval predictor equivalent to a given interval observer is derived. Moreover, because the wrapping effect could be avoided by tuning properly the interval observer, we can find an equivalent ¿ - order interval predictor such that it also avoids the wrapping effect. Finally, an example based on an industrial servo actuator will be used to illustrate the derived results. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft