Design of residual generators and adaptive filters for the FDI of aircraft model sensors

Castaldi, Paolo; Geri, Walter; Bonfè, Marcello; Simani, Silvio; Benini, M.

doi:10.1016/j.conengprac.2008.11.006

Cited by 50 publications

(50 citation statements)

References 17 publications

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“…The proofs are similar to those by Castaldi et al (2010) and have been omitted here. In order to design the NLGA-AF scheme, it is possible to design a 4 NLGA adaptive filter in the form of (23)-(25), allowing estimating the magnitude of a step fault acting on the linear force actuator of the inverted pendulum, as shown in (7).…”

mentioning

confidence: 49%

Active fault tolerant control of nonlinear systems: The cart-pole example

Bonfè¹,

Castaldi²,

Mimmo³

et al. 2011

International Journal of Applied Mathematics and Computer Science

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This paper describes the design of fault diagnosis and active fault tolerant control schemes that can be developed for nonlinear systems. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via the nonlinear geometric approach, which allows obtaining the disturbance de-coupling property. The controller reconfiguration exploits directly the on-line estimate of the fault signal. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the nonlinear disturbance de-coupling method based on the nonlinear differential geometry, as well as the feasibility and efficiency of the proposed approach. Extensive simulations of the benchmark process and Monte Carlo analysis are practical tools for assessing experimentally the robustness and stability properties of the developed fault tolerant control scheme, in the presence of modelling and measurement errors. The fault tolerant control method is also compared with a different approach relying on sliding mode control, in order to evaluate benefits and drawbacks of both techniques. This comparison highlights that the proposed design methodology can constitute a reliable and robust approach for application to real nonlinear processes.

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mentioning

confidence: 49%

Active fault tolerant control of nonlinear systems: The cart-pole example

Bonfè¹,

Castaldi²,

Mimmo³

et al. 2011

International Journal of Applied Mathematics and Computer Science

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show abstract

“…n(x), (x), the columns of g(x), and p d (x) are smooth vector fields, with h(x) a smooth map. The development of the NLGA strategy for the design of the estimator for the fault f with the decoupling of the disturbance d is based on the procedure presented in [34]. It was shown that the considered NLGA scheme extended to the fault diagnosis problem is based on a coordinate change in the state space and in the output space, such that, by using the new (local) state and output coordinates (x,ȳ), the system of Eq.…”

Section: Model-based Fault Diagnosis Methodsmentioning

confidence: 99%

“…It was shown that the considered NLGA scheme extended to the fault diagnosis problem is based on a coordinate change in the state space and in the output space, such that, by using the new (local) state and output coordinates (x,ȳ), the system of Eq. (38) is transformed into [34]:…”

Section: Model-based Fault Diagnosis Methodsmentioning

confidence: 99%

Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

Simani¹,

Castaldi²,

Farsoni³

2017

Preprint

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Abstract:The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, 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 farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

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“…However, other diagnosis signals may be constructed and evaluated (Isermann, 2006). There has been a lot of research on actuator-fault diagnosis and also some fewer works on sensor-fault diagnosis (e.g., Castaldi et al, 2010). A list of techniques that are commonly encountered in the literature for the design of fault detection and isolation (FDI) systems can be found, e.g., in the works of Ducard (2009) and Alwi et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

Ducard

2015

International Journal of Applied Mathematics and Computer Science

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This article presents a single model active fault detection and isolation system (SMAC-FDI) which is designed to efficiently detect and isolate a faulty actuator in a system, such as a small (unmanned) aircraft. This FDI system is based on a single and simple aerodynamic model of an aircraft in order to generate some residuals, as soon as an actuator fault occurs. These residuals are used to trigger an active strategy based on artificial exciting signals that searches within the residuals for the signature of an actuator fault. Fault isolation is carried out through an innovative mechanism that does not use the previous residuals but the actuator control signals directly. In addition, the paper presents a complete parameter-tuning strategy for this FDI system. The novel concepts are backed-up by simulations of a small unmanned aircraft experiencing successive actuator failures. The robustness of the SMAC-FDI method is tested in the presence of model uncertainties, realistic sensor noise and wind gusts. Finally, the paper concludes with a discussion on the computational efficiency of the method and its ability to run on small microcontrollers.

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Design of residual generators and adaptive filters for the FDI of aircraft model sensors

Cited by 50 publications

References 17 publications

Active fault tolerant control of nonlinear systems: The cart-pole example

Active fault tolerant control of nonlinear systems: The cart-pole example

Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

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Design of residual generators and adaptive filters for the FDI of aircraft model sensors

Cited by 50 publications

References 17 publications

Active fault tolerant control of nonlinear systems: The cart-pole example

Active fault tolerant control of nonlinear systems: The cart-pole example

Data&ndash;Driven Fault Diagnosis of a Wind Farm Benchmark Model

Smac–Fdi: A Single Model Active Fault Detection and Isolation System for Unmanned Aircraft

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Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model