Local variation detection in MDOF system using wavelet based transmissibility and its application in cracked beam

Since the local stiffness or damping variation happens when damage occurs in engineer structures, it is useful to detect the local variation as a way for structural damage inspection. As a vibration based approach, transmissibility has attracted considerable interest because of its convenience and effectiveness in damage detection. However, using the traditional Fourier transform, it should be very careful to select the frequency bands in transmissibility calculation. Inappropriate choice of frequency band could cause a complete inaccurate result. For unknown damage detection, it is difficult to select the frequency band which eigen-frequency should be included. This paper proposes a novel method using wavelet based transmissibility for local variation detection. Benefiting from the ability in subtle information acquisition of wavelet transform, it is useful in reducing the influence of frequency bands to the indicators. Analytical derivation using wavelet balance method and numerical studies of a multiple degree of freedom (MDOF) system are carried out to verify the effectiveness of the proposed method. In the last section, the method is applied for detecting crack position in cantilever beam with analysis of its sensitivity to frequency bands and measurement inaccuracies.

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A New Transmissibility Based Indicator of Local Variation in Structure and Its Application for Damage Detection

Shock and Vibration ◽

10.1155/2015/850286 ◽

2015 ◽

Vol 2015 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

X. Z. Li ◽

Z. K. Peng ◽

X. J. Dong ◽

W. M. Zhang ◽

G. Meng

Keyword(s):

Damage Detection ◽

Local Variation ◽

New Method ◽

Engineering Structures ◽

Engineering Structure ◽

Frequency Bands ◽

Numerical Studies ◽

Novel Method ◽

Stiffness And Damping ◽

Crack Position

The presence of damage in engineering structures could usually cause local variation in stiffness or damping, and therefore it is meaningful to detect the variation as early as possible for protecting the engineering structure from serious damage. In the study, a novel method is developed to detect and locate the local variation in stiffness and damping for structures based on transmissibility. Some important properties of transmissibility are first analytically revealed and, further, a variation detection indicator is proposed to locate the variation. The effectiveness of the proposed method is verified by numerical studies and the usefulness of it is demonstrated by application for detecting crack position in beam structures. The results show that the proposed new method has better performance than the three conventional transmissibility based methods when considering different frequency bands and noise.

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Genetic Algorithms in Structural Identification and Damage Detection

Intelligent Computational Paradigms in Earthquake Engineering ◽

10.4018/978-1-59904-099-8.ch013 ◽

2007 ◽

pp. 316-341 ◽

Cited By ~ 1

Author(s):

Chan Koh

Keyword(s):

Genetic Algorithms ◽

Damage Detection ◽

Structural Damage ◽

Structural Identification ◽

Search Space ◽

Degree Of Freedom ◽

Structural Damage Detection ◽

Space Reduction ◽

Numerical Studies ◽

Before And After

Genetic algorithms (GA) have proved to be a robust, efficient search technique for many problems. In this chapter, the latest developments by the authors in the area of structural identification and structural damage detection using genetic algorithms are presented. A GA strategy involving a search space reduction method (SSRM) using a modified genetic algorithm based on migration and artificial selection (MGAMAS) is first used to identify structural properties in multiple degree-of-freedom systems. The SSRM is then incorporated in a structural damage detection strategy using response measurements both before and after damage has taken place. Numerical studies on 10 and 20 degree-of-freedom systems show that a small damage of only 2.5% can be accurately and consistently identified from incomplete acceleration measurements in the presence of 5% input and output noise.

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Structural Damage Detection Using Curvature Mode Difference Curve

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.250-253.1248 ◽

2011 ◽

Vol 250-253 ◽

pp. 1248-1251 ◽

Cited By ~ 1

Author(s):

Hang Jing ◽

Ling Ling Jia ◽

Yi Zhao

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Damage Identification ◽

Numerical Differentiation ◽

Engineering Structures ◽

System Parameters ◽

Numerical Studies ◽

Damage Indicator ◽

Structural Damage Identification ◽

Changing Rate

Damage detection in civil engineering structures using the dynamic system parameters has become an important area of research. The sensitivity of damage indicator is of great value to structural damage identification. The curvature mode is an excellent parameter in damage detection of structures, while in case that certain curvature mode curve can’t show existence of damage. In this paper, numerical studies are conducted to demonstrate the deficiency of curvature mode to damage detection. Then a new damage indicator called “curvature mode changing rate” (CMCR) is introduced which is processed by numerical differentiation of curvature mode curve. The simulation results show that the new index is superior to curvature mode for structural damage identification, and it is still sensitive to the damaged location in the mode node.

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Damage Detection in Initially Nonlinear Structures Based on Variational Mode Decomposition

International Journal of Structural Stability and Dynamics ◽

10.1142/s0219455420420092 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2042009

Author(s):

Yu Xin ◽

Jun Li ◽

Hong Hao

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Dynamic Responses ◽

Modal Parameters ◽

Nonlinear Structures ◽

Variational Mode Decomposition ◽

Structural Dynamic ◽

Numerical Studies ◽

Mode Decomposition ◽

Frequency Domain Methods

Nonlinear characteristics in the dynamic behaviors of civil structures degrade the performance of damage detection of the linear theory based traditional time- and frequency-domain methods. To overcome this challenge, this paper proposes a damage detection approach for nonlinear structures based on Variational Mode Decomposition (VMD). In this approach, the measured dynamic responses from nonlinear structures under earthquake excitations are adaptively decomposed into a finite number of monocomponents by using VMD. Each decomposed mono-component represents an amplitude modulated and frequency modulated (AMFM) signal with a limited frequency bandwidth. Hilbert transform is then employed to identify the instantaneous modal parameters of the decomposed monomodes, including instantaneous frequencies and mode shapes. Based on the identified modal parameters from the decomposed structural dynamic responses, two damage indices are defined to identify the location and severity of structural damage, respectively. To validate the effectiveness and accuracy of the proposed approach, a nonlinear seven-storey shear building model with four different damage cases under earthquake excitations is used in the numerical studies. In experimental verifications, data from shake table tests on a 12-storey scaled reinforced concrete frame structure with different earthquake excitations are analyzed with the proposed approach. The results in both numerical studies and experimental validations demonstrate that the proposed approach can be successfully applied for nonlinear structural damage identification.

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A Hierarchical Bayesian Method for Time Domain Structure Damage Detection

Volume 8: 31st Conference on Mechanical Vibration and Noise ◽

10.1115/detc2019-97026 ◽

2019 ◽

Author(s):

Wei Feng ◽

Qiaofeng Li ◽

Qiuhai Lu

Keyword(s):

Damage Detection ◽

Time Domain ◽

Structural Damage ◽

Engineering Practice ◽

Hierarchical Bayesian ◽

Structure Damage ◽

Virtual Forces ◽

Hierarchical Bayesian Method ◽

External Forces ◽

Local Stiffness

Abstract A time domain structural damage detection method based on hierarchical Bayesian framework is proposed. Due to local stiffness reductions, the responses of damaged structures vary from those in undamaged status under the same external excitation. In this paper, the responses of damaged structures are assumed as the result of a summation of known external forces and unknown virtual forces exerted on corresponding undamaged structures. The damages can thus be detected, located, and quantified by the identification of associated virtual forces. A hierarchical Bayesian formulation considering all undetermined damage-related variables is adopted for the identification of virtual forces. The reasonable values of the variables and their uncertainties are depicted by their posterior distributions, sampled by Markov chain Monte Carlo method. Compared with traditional Bayesian formulations, manual choice of prior parameters is avoided and less prior information is required. The proposed virtual force indicator provides a more intuitive perspective for damage detection tasks and is potentially more operable in engineering practice. These advantages are illustrated by simulation of a cantilever beam under various damage conditions.

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A Novel Method for Extracting Maximum Kurtosis Component and Its Applications in Rolling Bearing Fault Diagnosis

Shock and Vibration ◽

10.1155/2019/8218237 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17

Author(s):

Yonggang Xu ◽

Zeyu Fan ◽

Kun Zhang ◽

Chaoyong Ma

Keyword(s):

Fault Diagnosis ◽

Frequency Band ◽

Rolling Bearing ◽

Rolling Bearings ◽

Frequency Bands ◽

Bearing Fault Diagnosis ◽

Important Index ◽

Novel Method ◽

Simulation Signal ◽

Fault Information

Rolling bearing plays an important role in the overall operation of the mechanical system; therefore, it is necessary to monitor and diagnose the bearings. Kurtosis is an important index to measure impulses. Fast Kurtogram method can be applied to the fault diagnosis of rolling bearings by extracting maximum kurtosis component. However, the final result may disperse the effective fault information to different frequency bands or find wrong frequency band, resulting in inaccurate frequency band selection or misdiagnosis. In order to find the maximum component of kurtosis accurately, an algorithm of frequency band multidivisional and overlapped based on EWT (MDO-EWT) is proposed in this paper. This algorithm changes the traditional Fast Kurtogram frequency bands division method and filtering method. It builds the EWT boundaries based on the maximum kurtosis component in each iteration and finally obtains the maximum kurtosis component. Through the simulation signal and the rolling bearing inner and outer ring fault signals verification, it is proved that the proposed method has a good performance on accuracy and effectiveness.

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Structural Damage Detection and Identification Using Learning Vector Quantization Neural Network

Volume 6: International Symposium on Motion and Vibration Control ◽

10.1115/detc99/movic-8401 ◽

1999 ◽

Author(s):

Qinzhong Shi ◽

Ichiro Hagiwara ◽

Toshiaki Sekine

Keyword(s):

Neural Network ◽

Damage Detection ◽

Vector Quantization ◽

Structural Damage ◽

Structural Parameters ◽

Learning Vector Quantization ◽

Structural Damage Detection ◽

Practical Application ◽

Detection And Identification ◽

Local Stiffness

Abstract This research deals with the structural damage detection by experimental measured modal parameters, such as the modal frequencies and the modal shapes. Changes of local structural parameters, induced by damage, will affect the local stiffness and cause the change of modal frequencies and modal shapes of structure. Use of these observable values to detect the damage of the structure is feasible and implement. Learning Vector Quantization (LVQ) Neural Network based on pattern classifier is used to detect the location of damage, and a method of releasing the dense of input vector to neural network is proposed to increase the accuracy of detection. Several numerical examples show the proposed method is effective to increase the rate of damage detection. Finally, a practical application example of damage detection for a turbine blade is used to demonstrate and verify the approach developed.

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