Search

The problem of parametric output-only identification of time-varying structures in a recursive manner is considered. A kernelized time-dependent autoregressive moving average (TARMA) model is proposed by expanding the time-varying model parameters onto the basis set of kernel functions in a reproducing kernel Hilbert space. An exponentially weighted kernel recursive extended least squares TARMA identification scheme is proposed, and a sliding-window technique is subsequently applied to fix the computational complexity for each consecutive update, allowing the method to operate online in time-varying environments. The proposed sliding-window exponentially weighted kernel recursive extended least squares TARMA method is employed for the identification of a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudo-linear regression TARMA method via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics. Furthermore, the comparisons demonstrate the superior achievable accuracy, lower computational complexity and enhanced online identification capability of the proposed kernel recursive extended least squares TARMA approach.

The problem of linear time-varying(LTV) system modal analysis is considered based on time-dependent state space representations, as classical modal analysis of linear time-invariant systems and current LTV system modal analysis under the “frozen-time” assumption are not able to determine the dynamic stability of LTV systems. Time-dependent state space representations of LTV systems are first introduced, and the corresponding modal analysis theories are subsequently presented via a stability-preserving state transformation. The time-varying modes of LTV systems are extended in terms of uniqueness, and are further interpreted to determine the system’s stability. An extended modal identification is proposed to estimate the time-varying modes, consisting of the estimation of the state transition matrix via a subspace-based method and the extraction of the time-varying modes by the QR decomposition. The proposed approach is numerically validated by three numerical cases, and is experimentally validated by a coupled moving-mass simply supported beam experimental case. The proposed approach is capable of accurately estimating the time-varying modes, and provides a new way to determine the dynamic stability of LTV systems by using the estimated time-varying modes.

This paper investigates the problem of output-only modal parameter estimation of time-varying structures. A two-stage least square method for modal parameter estimation of time-varying structures is presented, consisting of a least square complex frequency-domain stage for the modal parameter estimation of all discrete time samples and a weighted least square stage for the estimation of continuous-time-represented modal parameters. This paper introduces a fuzzy-clustering-based approach for validating and sifting the modal parameters and develops four modal assurance criterion-based distance functions to improve the quality of clustering of operational mode shapes. Furthermore, the proposed method has been validated by a simulation example and a laboratory experiment.

This paper investigates the problem of modal parameter estimation of time-varying structures under unknown excitation. A time–frequency-domain maximum likelihood estimator of modal parameters for linear time-varying structures is presented by adapting the frequency-domain maximum likelihood estimator to the time–frequency domain. The proposed estimator is parametric, that is, the linear time-varying structures are represented by a time-dependent common-denominator model. To adapt the existing frequency-domain estimator for time-invariant structures to the time–frequency methods for time-varying cases, an orthogonal polynomial and z -domain mapping hybrid basis function is presented, which has the advantageous numerical condition and with which it is convenient to calculate the modal parameters. A series of numerical examples have evaluated and illustrated the performance of the proposed maximum likelihood estimator, and a group of laboratory experiments has further validated the proposed estimator.

An hybrid real coded Genetic Algorithm with damage penalization is implemented to locate and quantify structural damage. Genetic Algorithms provide a powerful tool to solved optimization problems. With an appropriate selection of their operators and parameters they can potentially explore the entire solution space and reach the global optimum. Here, the set-up of the Genetic Algorithm operators and parameters is addressed, providing guidelines to their selection in similar damage detection problems. The performance of five fundamental functions based on modal data is studied. In addition, this paper proposes the use of a damage penalization that satisfactorily avoids false damage detection due to experimental noise or numerical errors. A tridimensional space frame structure with single and multiple damages scenarios provides an experimental framework which verifies the approach. The method is tested with different levels of incompleteness in the measured degrees of freedom. The results show that this approach reaches a much more precise solution than conventional optimization methods. A scenario of three simultaneous damage locations was correctly located and quantified by measuring only a 6.3% of the total degrees of freedom. ispartof: Mechanical Systems and Signal Processing vol:25 issue:5 pages:1559-1573 status: published

The detection of damage with model-based methods is a constrained nonlinear optimization problem. Conventional optimization approaches usually lead to local minima. Furthermore, they are highly sensitive to experimental noise or numerical errors. Genetic algorithms (GAs) provide an attractive alternative since they can potentially explore the entire solution space and reach the global optimum. However, GAs are inherently slow when they work with complicated or time consuming objective functions. To overcome this problem parallel GAs are proposed, and they are particularly easy to implement and provide a superior numerical performance. In this study, a real-coded parallel GA is implemented to detect structural damage. The objective function is based on operational modal data; it considers the initial errors in the numerical model. False damage detection is avoided by using damage penalization. The algorithm is verified with two experimental cases. First, a test structure of an airplane subjected to three increasing levels of damage. Second, a multiple cracked reinforced concrete beam that is subjected to a nonsymmetrical increasing static load to introduce cracks. In both cases, the detected damage has a good correspondence with the experimental damage.

This work introduces a resonant-based, mixed numerical–experimental method for the determination of the in-plane elastic properties of the constituent materials of laminates. This non-destructive method identifies elastic properties from the resonant frequencies of beam-shaped layered specimens, using a set of finite element models. The method is demonstrated on a thermal barrier coating system made of NiCoCrAlY bondcoat and yttria-stabilised zirconia topcoat deposited by air-plasma spraying on stainless steel. The stainless steel was found to be elastically anisotropic, while both bondcoat and topcoat exhibited in-plane isotropy. Moreover, the topcoat Poisson's ratio approached zero, and the bondcoat properties varied with the coating thickness. Scanning electron microscopy was used to correlate the identified elastic properties with the coating microstructure.

The elastic and damping properties of two different soft magnetic composite (SMC) materials processed by a powder metallurgy route (Somaloy ™ 500 + 0.5 wt.% Kenolube and Somaloy ™ 500 + 0.6 wt.% LB1) have been characterised by means of non-destructive resonant vibration analysis, using the impulse excitation technique (IET). Microstructural characterisation by means of X-ray diffraction (XRD) and scanning electron microscopy (SEM) was carried out in order to understand the structural heterogeneity revealed with IET. A simple structural finite element model confirmed the link between microstructural heterogeneity of the SMC bodies and their vibration behaviour.

Mixed numerical-experimental techniques (MNETs) combine experimental test results and numerical modelling techniques with the goal of identifying physical properties. Experimental results always come with a level of uncertainty. This input uncertainty will migrate through the MNET routine, and will result in an uncertainty on the identified parameters. Therefore, MNET procedures should not only provide an estimated value for the physical properties, they should also provide additional information about the reliability of the results obtained. This paper presents a routine that is able to transform the uncertainty on the input parameters of a MNET into the uncertainty on the output parameters. The approach does not require any change of the initial MNET routine, it is just an additional computational step that has to be performed after the MNET has identified the deterministic values of the unknown physical properties. The routine is demonstrated on a vibration based MNET used to identify elastic material properties.

This paper compares results of three different methods to determine the in-plane elastic properties of sheet materials. Results obtained with standard resonant beam and tensile tests are used to assess a mixed numerical–experimental technique developed to determine the in-plane elastic properties of orthotropic plates from the resonance frequencies of rectangular plate samples (the so-called ‘Resonalyser’ technique). Test materials were selected on the basis of an expected low degree of elastic anisotropy in order to put the accuracy and sensitivity of the different techniques to assess anisotropic materials to a test. Therefore, aluminium alloy and stainless steel samples were prepared from hot-rolled plates, deliberately avoiding pronounced cold-rolling textures. The differences between the results obtained with the three experimental approaches are critically evaluated. In the case of very thin plates, the existing mixed numerical–experimental Resonalyser procedure succeeded in accurately identifying the elastic material properties. A slightly adapted procedure is proposed to extend the applicability of the Resonalyser procedure to thicker plates.

Output-only modal analysis allows the estimation of modal models for structures in their real in-operation conditions. One of the main drawbacks of this approach is that a part of the modal model can no longer be estimated. As a result, the operational mode shape estimates remain incorrectly scaled, dependent on the unknown level of ambient excitation. This incompleteness somewhat restricts the applicability of in-operation modal models. No techniques were previously available for the normalisation of operational mode shapes purely based on experimental output-only data. Recently, a sensitivity-based normalisation approach, that can be used for output-only data, was proposed and successfully tested on simple laboratory structures. The principle behind the normalisation technique is the interpretation of the shifts in modal parameters induced by well-known structural modifications. In this contribution, the sensitivity-based normalisation approach was experimentally validated on a basis of vibration experiments performed on a tractor-sprayer combination. (C) 2003 Silsoe Research Institute. All rights reserved Published by Elsevier Science Ltd.

It is a well-known fact that the linear dynamic behaviour of mechanical structures can be studied by modelling the relation between force(s) input(s)], acting on the structure, and their resulting structural vibration response(s) [output(s)]. For industrial structures, in their real in-operation conditions, it often becomes hard (or impossible) to experimentally measure the excitation. For this reason, attention has been paid to the development of system identification techniques that work on a basis of response data only. The use of such techniques allows the identification of modal models for structures excited by unknown ambient noise and vibration. In this contribution, controlled vibration experiments were conducted on a sprayer boom that was mounted on a tractor. A comparison was made between the results of a classic input-output and output-only modal analysis based on maximum likelihood system identification techniques. Due to the interaction between the dynamic artificial excitation device and the test structure, the output-only approach modelled the excitation device together with the test structure. The identified output-only modal parameters were validated by direct comparison to the identification results obtained on a classic input-output transfer function data set between the generator input signal and the structural responses. (C) 2003 Silsoe Research Institute. All rights reserved Published by Elsevier Science Ltd.

For rotating electric machines, the reluctance forces (Maxwell stresses) acting on the stator teeth are a major cause of noise emission. Next to the reluctance forces, magnetostriction is a potential cause of additional noise from electric machines. First, a thermal stress analogy is used to introduce magnetostriction in the finite-element framework. Next, we present the computation and comparison of the stator vibration spectra caused by these two effects separately, by example of a 45 kW induction machine. Moreover, two kinds of magnetostriction characteristics of the stator yoke material are compared: a quadratic /spl lambda/(B) curve and a /spl lambda/(B) curve with zero-crossing around 1.5 Tesla.

The magnetic material's deformation caused by magnetostriction is represented by an equivalent set of mechanical forces, giving the same deformation to the material as magnetostriction does. This is done in a way similar to how thermal stresses are usually incorporated. The resulting magnetostriction force distribution is summed to other force distributions (external mechanical forces, magnetic forces) before starting the mechanical deformation or vibration analysis. This procedure is incorporated into a weakly coupled cascade solving of the magnetomechanical problem.