Passive Robust Fault Detection of Dynamic Processes Using Interval Models

Puig, Vicenç; Quevedo, Joseba; Escobet, Teresa; Nejjari, Fatiha; Heras, Salvador De Las

doi:10.1109/tcst.2007.906339

Cited by 78 publications

(61 citation statements)

References 10 publications

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“…So far, many existing results have focused on adaptive threshold generation for linear systems [29][30][31]43]. However, the corresponding results for nonlinear systems have been scattered in spite of their engineering significance [24,34].…”

Section: Introductionmentioning

confidence: 99%

Filtering and fault detection for nonlinear systems with polynomial approximation

Liu

Wang

et al. 2015

Automatica

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This paper is concerned with polynomial filtering and fault detection problems for a class of nonlinear systems subject to additive noises and faults. The nonlinear functions are approximated with polynomials of a chosen degree. Different from the traditional methods, the approximation errors are not discarded but formulated as low-order polynomial terms with normbounded coefficients. The aim of the filtering problem is to design a least squares filter for the formulated nonlinear system with uncertain polynomials, and an upper bound of the filtering error covariance is found and subsequently minimized at each time step. The desired filter gain is obtained by recursively solving a set of Riccati-like matrix equations, and the filter design algorithm is therefore applicable for online computation. Based on the established filter design scheme, the fault detection problem is further investigated where the main focus is on the determination of the threshold on the residual. Due to the nonlinear and time-varying nature of the system under consideration, a novel threshold is determined that accounts for the noise intensity and the approximation errors, and sufficient conditions are established to guarantee the fault detectability for the proposed fault detection scheme. Comparative simulations are exploited to illustrate that the proposed filtering strategy achieves better estimation accuracy than the conventional polynomial extended Kalman filtering approach. The effectiveness of the associated fault detection scheme is also demonstrated.

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Section: Introductionmentioning

confidence: 99%

Filtering and fault detection for nonlinear systems with polynomial approximation

Liu

Wang

et al. 2015

Automatica

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“…One of the most developed families of approaches to deal with model uncertainty, called active, is based on generating residuals, which are insensitive to uncertainty (modeling errors and disturbances), while at the same time sensitive to faults using some decoupling method [24]. On the other hand, there is a second family of approaches, called passive, which enhances the robustness of the fault detection system at the decision-making stage using an adaptive threshold [25].…”

Section: A Motivationmentioning

confidence: 99%

“…In this paper, the uncertainty will be located in the parameters bounding their values by intervals using the so-called interval models [25]. The robustness in fault detection is achieved by means of the passive approach at the decisionmaking stage using an adaptive threshold generated by considering the set of model responses obtained by varying the uncertain parameters within their intervals.…”

Section: A Motivationmentioning

confidence: 99%

Fault Diagnosis of Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

Sanchez

Escobet

Puig

2014

IFAC Proceedings Volumes

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Abstract-This paper proposes a model-based fault diagnosis approach for wind turbines and its application to a realistic wind turbine fault diagnosis benchmark. The proposed fault diagnosis approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown but bounded description of the model parametric uncertainty and noise using the the so-called set-membership approach. This approach leads to formulate the fault detection test by means of checking if the measurements fall inside the estimated output interval, obtained from the mathematical model of the wind turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed fault diagnosis approach has been validated on a 5MW wind turbine using the NREL FAST simulator. The obtained results are presented and compared with other approaches proposed in the literature.Index Terms-Analytical redundant relations, interval-based observers, model-based fault diagnosis, wind turbines.

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“…In the last decades, interval state estimation for systems without fault has been extensively studied within a set-theoretic framework, see e.g. in Gouzé et al (2000); Puig et al (2008);Puig (2010); Efimov and Raïssi (2016); Chambon et al (2016) and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Zonotopic Fault Estimation Filter Design for Discrete-time Descriptor Systems * *This work has been partially funded by the Spanish Government and FEDER through the projects CICYT ECOCIS (ref. DPI2013-48243-C2-1-R), CICYT DEOCS (ref. DPI2016-76493-C3-3-R), CICYT HARCRICS (ref. DPI2014-58104-R) and by National Natural Science Foundation of China (Grant No. 61273162, 61403104).

Wang

Puig

et al. 2017

IFAC-PapersOnLine

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This paper considers actuator-fault estimation for discrete-time descriptor systems with unknown but bounded system disturbance and measurement noise. A zonotopic fault estimation filter is designed based on the analysis of fault detectability indexes. To ensure estimation accuracy, the filter gain in the zonotopic fault estimation filter is optimized through the zonotope minimization. The designed zonotopic filter not only can estimate fault magnitudes, but it also provides fault estimation results in an interval, i.e. the upper and lower bounds of fault magnitudes. Moreover, the proposed fault estimation filter has a non-singular structure and hence is easy to implement. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.

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Passive Robust Fault Detection of Dynamic Processes Using Interval Models

Cited by 78 publications

References 10 publications

Filtering and fault detection for nonlinear systems with polynomial approximation

Filtering and fault detection for nonlinear systems with polynomial approximation

Fault Diagnosis of Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

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