Identification for passive robust fault detection using zonotope‐based set‐membership approaches

Blesa, Joaquim; Puig, Vicenç; Saludes, Jordi

doi:10.1002/acs.1242

Cited by 58 publications

(49 citation statements)

References 26 publications

(56 reference statements)

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“…(Pudar &Ligget,1992). In general, demand and modelling uncertainty can be captured using historical data free of leaks and by trying to adjust uncertainty bounds in demand and in model using a bounding approach as described, for example, in (Blesa et al, 2011) estimation at these nodes using the calibrated DMA hydraulic model assuming a leakage-free scenario, 0 p (k) (Step 1).…”

Section: Integration Of the Leakage Localization Methods Into The Propmentioning

confidence: 99%

A decision support system for on-line leakage localization

Meseguer

Mirats-Tur

Cembraño

et al. 2014

Environmental Modelling & Software

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Abstract:This paper describes a model-driven decision-support system (software tool) implementing a model-based methodology for on-line leakage detection and localization which is useful for a large class of water distribution networks. Since these methods present a certain degree of complexity which limits their use to experts, the proposed software tool focuses on the integration of a method emphasizing its use by water network managers as a decision support system. The proposed software tool integrates a model-based leakage localization methodology based on the use of on-line telemetry information, as well as a water network calibrated hydraulic model. The application of the resulting decision support software tool in a district metered area (DMA) of the Barcelona distribution network is provided and discussed. The obtained results show that the leakage detection and localization may be performed efficiently reducing the required time.

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Section: Integration Of the Leakage Localization Methods Into The Propmentioning

confidence: 99%

A decision support system for on-line leakage localization

Meseguer

Mirats-Tur

Cembraño

et al. 2014

Environmental Modelling & Software

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“…The interval prediction method is introduced 18 to improve the robustness of the recursive subspace identification based on the variable forgetting factor algorithm. Combining the interval prediction with the recursive subspace identification based on the variable forgetting factor algorithm improves the robustness of fault diagnosis while avoiding system instability.…”

Section: Interval Predictionmentioning

confidence: 99%

A new fault diagnosis approach for the pitch system of wind turbines

Wen

2017

Advances in Mechanical Engineering

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To diagnose the fault in the pitch actuator of wind turbines which causes the change in pitch angles, a fault diagnosis method is proposed in this study. The method combines the interval prediction algorithm with the recursive subspace identification based on the variable forgetting factor algorithm. First, the recursive subspace identification based on the variable forgetting factor algorithm is used to estimate the linear parameter-varying model of the wind turbine, which solves the model uncertainty. Second, considering the boundary problem described by model uncertainties, the interval prediction algorithm is introduced based on the basis of the identification results. Moreover, whether the fault occurs or not is determined by judging whether the pitch angle is in the range of the upper and lower bounds. Thus, the robustness of fault detection is improved. Finally, the proposed approach is validated by simulation experiments.

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“…When ( , ) F k θ is linear, boxes, parallelotopes, ellipsoids or zonotopes are used to characterize the AFPS (Alamo, Bravo, and Camacho, 2005;Blesa, Puig, and Saludes, 2011). In the nonlinear case, a minimum outer box can be determined by means of a set of optimization problems (Milanese et al, 1996).…”

Section: Parameter Estimation Problemmentioning

confidence: 99%

Set-membership identification and fault detection using a Bayesian framework

Canti

Blesa

Puig

et al. 2014

International Journal of Systems Science

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Abstract-This paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the setmembership model estimation problem can be reformulated from a Bayesian viewpoint in order to, firstly, determine the feasible parameter set in the identification stage and, secondly, check the consistency between the measurement data and the model in the fault detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single output and multiple output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple tank process.

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Identification for passive robust fault detection using zonotope‐based set‐membership approaches

Cited by 58 publications

References 26 publications

A decision support system for on-line leakage localization

A decision support system for on-line leakage localization

A new fault diagnosis approach for the pitch system of wind turbines

Set-membership identification and fault detection using a Bayesian framework

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