A Novel Damage Indicator Based on the Electromechanical Impedance Principle for Structural Damage Identification

Zhou, Pin; Wang, Dansheng; Zhu, Hongping

doi:10.3390/s18072199

Cited by 18 publications

(13 citation statements)

References 48 publications

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“…Here, blue indicates the unloaded structure and warm colors represent loaded structural conditions. Similarly to related works, compare, for example [4,8,21,22], we consider a damage indicator (DI) to evaluate structural conditions. The DIs should have small values for the intact structure and increased values when damage occurs.…”

Section: Resultsmentioning

confidence: 99%

Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method

Moll

Schmidt

Käsgen

et al. 2020

Sensors

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This paper presents a proof of concept for simultaneous load and structural health monitoring of a hybrid carbon fiber rudder stock sample consisting of carbon fiber composite and metallic parts in order to demonstrate smart sensors in the context of maritime systems. Therefore, a strain gauge is used to assess bending loads during quasi-static laboratory testing. In addition, six piezoelectric transducers are placed around the circumference of the tubular structure for damage detection based on the electro-mechanical impedance (EMI) method. A damage indicator has been defined that exploits the real and imaginary parts of the admittance for the detection of pin failure in the rudder stock. In particular, higher frequencies in the EMI spectrum contain valuable information about damage. Finally, the information about damage and load are merged in a cluster analysis enabling damage detection under load.

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Section: Resultsmentioning

confidence: 99%

Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method

Moll

Schmidt

Käsgen

et al. 2020

Sensors

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“…Utilizing the RST lessens the properties for SVM activity. Rough Set Theory is one of information mining strategies which lessen the highlights from enormous quantities of information [9]. Utilizing RST needs to fabricate the choice table or the data table.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Data Mining Technique for Structural Strength Monitoring System

Rao¹,

Chaparala²

2021

ISI

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A fundamental target of strength monitoring frameworks for different structures is to analyze the condition of the structure and to assess its conceivable danger and furthermore to investigation, identification, and characterization of danger in complex structures is a critical part of auxiliary strength checking. The capacities are browsed as lexicon of time-recurrence movement and scaled variants of a basic Gaussian hypothesis work. This word reference is likewise adjusted to utilize genuine estimated information. Characterization is then accomplished by coordinating the removed damage includes in the time-frequency. In this paper, we utilize our model to assess our information mining approach for the fault checking. The balanced scratch-off and high-pass sifting strategies are consolidated adequately to take care of basic issues in numerical reconciliation signs gathered from sensors are disintegrated into direct blends of very confined Gaussian capacities utilizing the coordinating significance decay calculation. The combination exactness is enhanced and contrasted with former numerical integrators. Rough set analysis uses only internal knowledge and does not rely on prior model assumption as fuzzy set methods or probabilistic models do. In this manuscript a novel hybrid algorithm combining the features of Rough set Support vector machine (Rs-SVM) classified structures and Rough set Artificial Neural Network (Rs-ANN) classified structures are used. At long last the vertices of the structure of different types are connected and analysed by the Hybrid algorithm and furthermore to additionally enhance order execution, the data gathered from numerous sensors is incorporated utilizing a Bayesian sensor combination approach.

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“…Regarding the hybrid approach, on the other hand, new trials in recent days are briefly summarized. Above all, the fourth-order voltage statistical moments (FVSMs) method based on piezoelectric measurement with a spectral element model (SEM) was proposed [16]. The method successfully identified additional mass as well as crack damages from voltage output in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages

Kim

Min²,

Kim³

et al. 2019

Sensors

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There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

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A Novel Damage Indicator Based on the Electromechanical Impedance Principle for Structural Damage Identification

Cited by 18 publications

References 48 publications

Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method

Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method

An Efficient Data Mining Technique for Structural Strength Monitoring System

Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages

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