Vibration Monitoring by Eigenstructure Change Detection Based on Perturbation Analysis**This work has been supported by the ITEA2 MODRIO project.

Döhler, Michael; Zhang, Qinghua; Mevel, Laurent

doi:10.1016/j.ifacol.2015.12.261

Cited by 4 publications

(8 citation statements)

References 18 publications

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“…Th choice of the parametrization θ is different than in [Döhler et al, 2015], where the eigenvector parts only at the sensor coordinates were part of the parametrization, computed by Hψ i . Though the nominal parameter in [Döhler et al, 2015] can be obtained entirely from measurements without the knowledge of the structural system matrices M, C and K, it allows only for change detection, while no link to the physical properties of the structure was given for fault isolation. In the current paper, a link from θ to the physical parameter set η will be made, which allows for the isolation of the physical parameter subset that is responsible for the detected change.…”

Section: Problem Statementmentioning

confidence: 99%

“…In [Döhler et al, 2015] an alternative method for parametric change detection has been developed, where the multiplicative eigenstructure change detection problem is transformed to an additive one by means of a perturbation analysis, assuming small parameter changes. Amongst others, this allows addressing random uncertainties more efficiently in associated hypothesis testing tools by avoiding the covariance matrix estimation problem encountered in the local asymptotic approach.…”

Section: Introductionmentioning

confidence: 99%

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Change detection and isolation in mechanical system parameters based on perturbation analysis

2017

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The monitoring of mechanical systems aims at detecting damages at an early stage, in general by using output-only vibration measurements under ambient excitation. In this paper, a method is proposed for the detection and isolation of small changes in the physical parameters of a linear mechanical system. Based on a recent work where the multiplicative change detection problem is transformed to an additive one by means of perturbation analysis, changes in the eigenvalues and eigenvectors of the mechanical system are considered in the first step. In a second step, these changes are related to physical parameters of the mechanical system. Finally, another transformation further simplifies the detection and isolation problem into the framework of a linear regression subject to additive white Gaussian noises, leading to a numerically efficient solution of the considered problems. A numerical example of a simulated mechanical structure is reported for damage detection and localization.

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Change detection and isolation in mechanical system parameters based on perturbation analysis

2017

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“…First, exponential convergence is ensured in our case when A is affine in θ as opposed to an ultimate boundedness property in [7]. Second, the proposed solution is computationally lighter, since [7] requires the use of both a state-observer for the nominal parameter values and of an adaptive observer, while we only need the latter.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, we aim at designing an adaptive observer for system (1). This problem arises in many practical applications, including electrochemical batteries [11] or mechanical systems [7] to cite a couple of examples, as soon as we need to estimate on-line both the unmeasured state and unknown parameters of a plant for monitoring purposes for instance.…”

Section: Introductionmentioning

confidence: 99%

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Local Adaptive Observer for Linear Time-Varying Systems with Parameter-Dependent State Matrices

Zhang

2018

2018 IEEE Conference on Decision and Control (CDC)

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We present an adaptive observer for linear timevarying systems whose state matrix depends on unknown parameters. We first assume that the state matrix is affine in these parameters. In this case, the proposed observer generates state and parameter estimates, which exponentially converge to the plant state and the true parameter, respectively, provided a persistence of excitation condition holds and the unknown parameters lie in a neighborhood of some known nominal values. Hence, some prior knowledge on the unknown parameters is required, but not on the state. We then modify the adaptive observer and its convergence analysis to systems whose state matrix is smooth, instead of being affine, in the unknown parameters. The convergence is approximate, and no longer exponential, in this case. An example is provided to illustrate the results, for which the required distance between the unknown parameters and their nominal values is investigated numerically.

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Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

Milovančević

Nikolić

Anđelković

2017

Mechanical Systems and Signal Processing

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Vibration Monitoring by Eigenstructure Change Detection Based on Perturbation Analysis**This work has been supported by the ITEA2 MODRIO project.

Cited by 4 publications

References 18 publications

Change detection and isolation in mechanical system parameters based on perturbation analysis

Change detection and isolation in mechanical system parameters based on perturbation analysis

Local Adaptive Observer for Linear Time-Varying Systems with Parameter-Dependent State Matrices

Analyses of the most influential factors for vibration monitoring of planetary power transmissions in pellet mills by adaptive neuro-fuzzy technique

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