Model Weight and State Estimation for Multiple Model Systems Applied to Fault Detection and Identification

Hallouzi, Redouane; Verhaegen, Michel; Babuška, Robert; Kanev, Stoyan

doi:10.3182/20060329-3-au-2901.00100

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…The model weight estimation results obtained with the combined state and weight estimation algorithm (Hallouzi et al, 2006) are given in Figure 3. In this figure, the time interval in which a deviation can be expected from the nominal situation (µ (1) = 1 and µ (2) , .…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the fault information will be obtained by estimation of the weights of the models in the model set. The weights will be estimated together with the state by an algorithm largely based on the method proposed in (Hallouzi et al, 2006). However, the main focus of this paper is assessing the FDI capabilities of the proposed fault modeling strategy, therefore the reader interested in the model weight estimation algorithm is referred to the aforementioned reference.…”

Section: Introductionmentioning

confidence: 99%

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Model Weight Estimation for Fdi Using Convex Fault Models

Hallouzi

Verhaegen

Kanev

2006

IFAC Proceedings Volumes

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Weight Estimation for Fdi Using Convex Fault Models

Hallouzi

Verhaegen

Kanev

2006

IFAC Proceedings Volumes

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“…Subsequently, the system is transformed into a state space representation, such that classic state feedback control can be applied (Hallouzi et al, 2006):…”

Section: Repetitive Controlmentioning

confidence: 99%

Data-driven repetitive control: Wind tunnel experiments under turbulent conditions

Frederik

Kröger

Gülker

et al. 2018

Control Engineering Practice

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A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven IPC methodology called Subspace Predictive Repetitive Control (SPRC) is employed. The effectiveness of SPRC will be demonstrated on a scaled 2-bladed wind turbine. An open-jet wind tunnel with an innovative active grid is employed to generate reproducible turbulent wind conditions. A significant load reduction with limited actuator duty is achieved even under these high turbulent conditions. Furthermore, it will be demonstrated that SPRC is able to adapt to changing operating conditions.

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“…In [25] also an attempt is made to incorporate the errors of the estimates. This attempt resulted in modified versions of (23) and (28), which had to be linearized to allow the two estimation steps to be linear again. However, it was concluded that the performance of the resulting algorithm was not better than the algorithm that used direct substitution of the current estimates.…”

Section: Covariance Propagationmentioning

confidence: 99%

“…In this section, the direct approach in which the state and model weights are jointly estimated is adopted. A novel algorithm for this nonlinear estimation problem is proposed in [28].…”

Section: Ekfmentioning

confidence: 99%

Multiple model estimation: A convex model formulation

Hallouzi

Verhaegen

Kanev

2008

Adaptive Control & Signal

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The multiple model (MM) framework provides an elegant solution to adaptive filtering problems. An important issue in the MM framework is how the estimation is performed. In this paper, a brief overview is given of the mainstream methods for MM estimation and a new method is proposed. Contrary to existing methods that mostly adopt a hybrid model structure, the newly proposed method uses a more general MM framework that allows for weighted combinations of the local models. The main advantage of this framework is that it has better model interpolation properties. These improved properties allow for smaller model sets, which are very useful in, for example, fault detection and identification (FDI) of partial faults. The improved interpolation properties are demonstrated by means of two simulation examples, one in which an FDI problem is addressed, and one in which a target tracking problem is addressed. Monte Carlo simulation results of these two examples are given. In these simulations, the well-known interacting IMM filter is compared with two estimation algorithms based on the proposed model structure.

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Model Weight and State Estimation for Multiple Model Systems Applied to Fault Detection and Identification

Cited by 12 publications

References 14 publications

Model Weight Estimation for Fdi Using Convex Fault Models

Model Weight Estimation for Fdi Using Convex Fault Models

Data-driven repetitive control: Wind tunnel experiments under turbulent conditions

Multiple model estimation: A convex model formulation

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