Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

Xu, Feng; Puig, Vicenç; Ocampo‐Martínez, Carlos; Olaru, Sorin; Niculescu, Silviu–Iulian

doi:10.1515/amcs-2017-0004

Cited by 20 publications

(8 citation statements)

References 18 publications

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“…In this case, these estimated signalsf i are directly exploited as diagnostic residuals r i , as remarked by Eq. (8). They can be compared with the thresholds of Eq.…”

Section: Fault Diagnosis Via Fuzzy Estimatorsmentioning

confidence: 99%

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Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System **

Simani¹,

Castaldi²

2018

Preprint

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The fault diagnosis of wind turbine systems represent a challenging issue, especially for offshore installations, thus justifying the research topics developed in this work. Therefore, this paper addresses the problem of the fault diagnosis of wind turbines, and it present viable solutions of fault detection and isolation techniques. The design of the so--called fault indicator consists of its estimate, which involves data--driven methods, as they result effective tools for managing partial analytical knowledge of the system dynamics, together with noise and disturbance effects. In particular, the suggested data--driven strategies exploit fuzzy systems and neural networks that are employed to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, as they approximate the dynamic evolution of the system along time. The designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of an offshore wind turbine plant. Finally, by taking into account the presence of uncertainty and disturbance implemented in the wind turbine simulator, the robustness and the reliability features of the proposed methods are also assessed. This aspect is fundamental when the proposed fault diagnosis methods have to be applied to offshore installations.

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“…In this case, these estimated signalsf i are directly exploited as diagnostic residuals r i , as remarked by Eq. (8). They can be compared with the thresholds of Eq.…”

Section: Fault Diagnosis Via Fuzzy Estimatorsmentioning

confidence: 99%

“…Fault diagnosis oriented to the sustainability feature when applied to safety-critical systems such as wind turbines has been proven to be a challenging issue [7,8], thus motivating the research topics addressed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System **

Simani¹,

Castaldi²

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Fault diagnosis oriented to the sustainability feature when applied to safetycritical systems such as wind turbines has been proven to be a challenging issue, see Byrski and Byrski [7] and Xu et al [8], thus motivating the research topics addressed in this chapter.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Techniques for a Wind Turbine System

Simani¹,

Castaldi²

2020

Fault Detection, Diagnosis and Prognosis

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The fault diagnosis and prognosis of wind turbine systems represent a challenging issue, thus justifying the research topics developed in this work with application to safety-critical systems. Therefore, this chapter addresses these research issues and demonstrates viable techniques of fault diagnosis and condition monitoring. To this aim, the design of the so-called fault detector relies on its estimate, which involves data-driven methods, as they result effective methods for managing partial information of the system dynamics, together with errors, model-reality mismatch and disturbance effects. In particular, the considered data-driven strategies use fuzzy systems and neural networks, which are employed to establish non-linear dynamic links between measurements and faults. The selected prototypes are based on non-linear autoregressive with exogenous input descriptions, since they are able to approximate non-linear dynamic functions with arbitrary degree of accuracy. The capabilities of the designed fault diagnosis schemes are verified via a high-fidelity simulator, which describes the normal and the faulty behaviour of a wind turbine plant. Finally, the robustness and the reliability features of the proposed methods are validated in the presence of uncertainty and disturbance implemented in the wind turbine simulator.

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“…Hence, the fault diagnosis in connection with the sustainable control of dynamic processes has been proven to be a challenging task (see, e.g., Hassanabadi et al, 2016;Byrski and Byrski, 2016;Xu et al, 2017), especially for energy conversion systems, such as wind turbines, and has thus motivated the research activities carried out through this paper.…”

Section: Introductionmentioning

confidence: 99%

Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark

Simani

Farsoni

Castaldi

2018

International Journal of Applied Mathematics and Computer Science

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This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

show abstract

Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

Cited by 20 publications

References 18 publications

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System **

Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System **

Fault Diagnosis Techniques for a Wind Turbine System

Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark

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