Model updating using correlation analysis of strain frequency response function

Guo, Ning; Yang, Zhichun; Jia, You; Wang, Le

doi:10.1016/j.ymssp.2015.09.036

Cited by 27 publications

(7 citation statements)

References 15 publications

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“…The stiffness and mass matrices are updated first, followed by updating with damping. More recently, Ning Guo et al [5] demonstrated that the strain-based FRF updating procedure is more accurate compared to a modal parameter updating procedure. Maletta et al [6] proposed a method, using a finite element model and Genetic Algorithms (GA), to obtain elastic constants of composite laminates.…”

Section: Introductionmentioning

confidence: 99%

Obtaining manufactured geometries of deep-drawn components through a model updating procedure using geometric shape parameters

Balla

Coox²,

Deckers³

et al. 2018

Mechanical Systems and Signal Processing

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The vibration response of a component or system can be predicted using the finite element method after ensuring numerical models represent realistic behaviour of the actual system under study. One of the methods to build high-fidelity finite element models is through a model updating procedure. In this work, a novel model updating method of deep-drawn components is demonstrated. Since the component is manufactured with a high draw ratio, significant deviations in both profile and thickness distributions occurred in the manufacturing process. A conventional model updating, involving Young's modulus, density and damping ratios, does not lead to a satisfactory match between simulated and experimental results. Hence a new model updating process is proposed, where geometry shape variables are incorporated, by carrying out morphing of the finite element model. This morphing process imitates the changes that occurred during the deep drawing process. An optimization procedure that uses the Global Response Surface Method (GRSM) algorithm to maximize diagonal terms of the Modal Assurance Criterion (MAC) matrix is presented. This optimization results in a more accurate finite element model. The advantage of the proposed methodology is that the CAD surface of the updated finite element model can be readily obtained after optimization. This CAD model can be used for carrying out analysis, as it represents the manufactured part more accurately. Hence, simulations performed using this updated model with an accurate geometry, will therefore yield more reliable results.

show abstract

Section: Introductionmentioning

confidence: 99%

Obtaining manufactured geometries of deep-drawn components through a model updating procedure using geometric shape parameters

Balla

Coox²,

Deckers³

et al. 2018

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

show abstract

“…Especially in methods based on the deep neural network [ 8 ], the requirement of the training sample amount is generally extensive. Unfortunately, adequate sample collection is extremely time-consuming in practice [ 9 , 10 ]. Insufficient data problems become a common obstacle in FR model updating [ 11 ] and reducing sample dependency would be desirable.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data

Deng

Zhang

Zhao

2020

Sensors

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Finite element model updating precision depends heavily on sufficient vibration feature extraction. However, adequate amount of sample collection is generally time-consuming in frequency response (FR) model updating. Accurate vibration feature extraction with insufficient data has become a significant challenge in FR model updating. To update the finite element model with a small dataset, a novel approach based on transfer learning is firstly proposed in this paper. A readily available fault diagnosis dataset is selected as ancillary knowledge to train a high-precision mapping from FR data to updating parameters. The proposed transfer learning network is constructed with two branches: source and target domain feature extractor. Considering about the cross-domain feature discrepancy, a domain adaptation method is designed by embedding the extracted features into a shared feature space to train a reliable model updating framework. The proposed method is verified by a simulated satellite example. The comparison results manifest that sample amount dependency has prominently lessened this method and the updated model outperforms the method without transfer learning in accuracy with the small dataset. Furthermore, the updated model is validated through dynamic response out of the training set.

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“…Of these methods and diagnostic tools, the following should be mentioned: identification of dynamic characteristics such as natural frequencies (NF) [3], mode shapes and their derivative parameters [4], time domain vibration response [5][6][7][8] frequency response functions [9], power spectral density [10], transmissibility functions [11], coherence functions [12], correlation functions [13], etc., which serves to identify the condition of an investigated object based on emitted vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Wheel rim state assessment using modal and geometrical parameters

Rychlik

2018

MATEC Web Conf.

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This paper presents the concept of a non-invasive method for assessing the wheel rim state to determine its technical status. The research procedure includes an analysis of the wheel rim frequency spectrum in a trial at a diagnostic station, which involves applying vibration forces with specific parameters on the wheel rim. The distribution of the wheel rim’s natural frequencies in rotation angle and unbalanced value with its positioning and radial run-out and axial run-out for the wheel rim bead in relation to the wheel disc centring hole on the internal and external wheel rim surfaces, are established based on the analysis and calculations. These parameters also serve to determine the condition of joints (fusion-welded, pressure-welded, etc.) and to identify the cracks or loss of integrity in the wheel rim structure. This method can be applied for tests of both new wheel rims during the quality control and used rims used for passenger cars, special vehicles and slow-running vehicles.

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Model updating using correlation analysis of strain frequency response function

Cited by 27 publications

References 15 publications

Obtaining manufactured geometries of deep-drawn components through a model updating procedure using geometric shape parameters

Obtaining manufactured geometries of deep-drawn components through a model updating procedure using geometric shape parameters

Transfer Learning Based Method for Frequency Response Model Updating with Insufficient Data

Wheel rim state assessment using modal and geometrical parameters

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