A new method for finite element model updating in structural dynamics

Zapico-Valle, J.L.; Alonso-Camblor, R.; Martínez, María Placeres González; García-Diéguez, M.

doi:10.1016/j.ymssp.2010.03.011

Cited by 36 publications

(22 citation statements)

References 11 publications

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“…Sarin et al [24] made comparative study residual errors between time-domain signals of simulations and experiments, and further provided a theoretical basis for model updating. Zapico-Valle et al [25] defined a residual value between FEM simulation and experimental signals to search the minimum residual value using optimization methods for the purpose of FEM model updating. Here, we directly use the cosine similarity between the time-domain vibration signals of FEM simulation and experimental measurement to update the FEM model of a bearing by adjusting the corresponding parameters.…”

Section: Cosine Similarity-based Fem Model Updating Techniquementioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

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Section: Cosine Similarity-based Fem Model Updating Techniquementioning

confidence: 99%

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

Liu

Huang

Xiang

2020

Sensors

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“…The validation of the mathematical model for a dynamical structure is a vital step in the process of structural analysis (Alvin et al 2003;Zapico-Valle et al 2010). The innovation of State-space models provides explicit techniques of linear multiple input multiple output (MIMO).…”

Section: System Identification Of Model Parameters From Experimental mentioning

confidence: 99%

A theoretical and experimental exploration of the seismic dynamics of multi-span bridges

Meibodi

Alexander

Norman

et al. 2020

Bull Earthquake Eng

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A generalized reduced-order model of a multi-span continuous bridge, on flexible discrete supports, that is subjected to multi-support seismic excitation is presented. This model highlights the key non-dimensional system parameters. Real spatiotemporal ground motion time-series (from the SMART-1 array, Taiwan) are used, as an alternative to employing artificial ground motion based on some spatial incoherence kernels. Benchmark experimental test data, using the multiple support excitation rig of a four-span bridge and SMART-1 array excitation, is used to validate/calibrate the proposed reduced-order model. An operational modal analysis is conducted to obtain least-square estimates of these key dynamic parameters using a Levenberg-Marquardt algorithm. The computationally efficient reduced-order model is then employed for a parametric study that explores the effect of spatial incoherence, bridge alignment and archetypal symmetrical and asymmetrical bridge geometries. A comparison of identical and multi-support excitation cases indicate the likely range of beneficial/adverse errors in neglecting the spatiotemporal nature of ground motions in design analyses.

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“…A small error in one input parameter can produce large deviations from the true structural responses. In order to obtain a more accurate and reliable finite element (FE) model of a structure, the FE model updating (or tuning) has been an active research area [7][8][9][10]. Usually combined with an optimization technique, an FE model updating procedure modifies the FE model and seeks to minimize the difference between the analysis and the experimental results.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Diamond Saw Blades Based on Experimentally Verified Finite Element Models

Chen¹,

Chang²,

Huang³

2012

Computer-Aided Design and Applications

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This paper proposes a two-phased procedure for the optimum design of circular diamond saw blades. In Phase one, an accurate finite element (FE) model representing an actual saw blade is acquired by incorporating experimental and finite element analysis (FEA) frequencies to update the blade FE model. In Phase two, shape optimization of the radial slots on the blade, based on the updated geometrical parameters obtained in Phase one, is performed to maximize the frequency separation between the FEA results and the saw's operational speed, in an attempt to reduce the possibility of structural resonance. The effectiveness of the proposed two-phased system is demonstrated by the successful implementation of several numerical examples.

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A new method for finite element model updating in structural dynamics

Cited by 36 publications

References 11 publications

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine

A theoretical and experimental exploration of the seismic dynamics of multi-span bridges

Optimum Design of Diamond Saw Blades Based on Experimentally Verified Finite Element Models

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