Structural Damage Detection in the Frequency Domain using Neural Networks

Lee, Jungwhee; Kim, Sungkon

doi:10.1177/1045389x06073640

Cited by 66 publications

(33 citation statements)

References 17 publications

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“…Dilena et al [68] presented the interpolation damage detection method using FRF measurements. Lee and Kim [69] proposed the signal anomaly index to express the amount of changes in the shape of FRFs or strain frequency response function (SFRF). Kim and Eun [70] compared the FRF-based approach extracted from dynamic measurements of a truss structure and the flexibility-based approach extracted from the modal data, and showed that the FRF-based approach can be utilized more explicitly than the flexibility-based approach.…”

Section: Frequency Domain Methodsmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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Abstract:Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

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Section: Frequency Domain Methodsmentioning

confidence: 99%

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

2017

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“…By comparing the FRFs of a sensing membrane with and without cell attachment, we can identify the features of adhesive cells. Many researchers have successfully employed neural networks on the measued FRF data for structural health monitoring and damage detection [11,12,13,14]. This paper presents the successful cell monitoring application.…”

Section: Introductionmentioning

confidence: 99%

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

Choudhury

Griffiths

et al. 2011

Biomed Microdevices

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A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. Distributive sensing scheme is designed to monitor the dynamics of the sensing structure.An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in a cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have unregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement, has great potential in biosensing applications.

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“…In this situation, a large amount of data representing the analysed problem is normalized in function of a mapping technique, leading to a faster training of the neural network and improved identification results. For more details refer to [4,6,12].…”

Section: Slanted Crack Identificationmentioning

confidence: 99%

An Experimental Approach for Crack Identification/Assessment in Piezoelectric Materials

Hattori¹,

Mustapha²,

Ye³

et al. 2014

Proceedings of 10th World Congress on Computational Mechanics

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Abstract. In this work, an experimental approach for the identification of through thickness cracks in piezoelectric materials is presented, where the electric potential on the surface of the PZT elements was measured at different locations before and after the crack was introduced. A damage index (DI) was defined, based on the amount of change in electrical potential between the intact and damaged states, which was later used to develop an algorithm for identifying and assessing cracks in a PZT element under a time harmonic load. Cracks of different lengths, depths and orientation were successfully identified.

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Structural Damage Detection in the Frequency Domain using Neural Networks

Cited by 66 publications

References 17 publications

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

An Experimental Approach for Crack Identification/Assessment in Piezoelectric Materials

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