Damage localization in metallic plate structures using edge-reflected lamb waves

Ebrahimkhanlou, Arvin; Dubuc, Brennan; Salamone, Salvatore

doi:10.1088/0964-1726/25/8/085035

Cited by 87 publications

(48 citation statements)

References 32 publications

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“…KEYWORDS microfiber coupler sensor, modal acoustic emission, source location, threshold, wavelet transform 1 | INTRODUCTION Source localization is extremely useful in various applications such as cracks analysis in aerospace, structure health monitoring in industry, and safety inspection in civil infrastructure. [1,2] Acoustic emission (AE) localization technique is one of the most popular schemes to locate damage or impact, which can be divided into two modes: active mode [3][4][5][6][7][8] and passive mode. [9] For passive mode, crack or impact is regarded as an acoustic source and generates the AE waves.…”

Section: Discussionmentioning

confidence: 99%

Source location using an optimized microfiber coupler sensor based on modal acoustic emission method

Wang

Liu

et al. 2017

Struct Control Health Monit

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Summary This paper demonstrates the application of an optimized microfiber coupler sensor (MFCS) to the source location with modal acoustic emission (MAE) method. First, based on analyzing the key influence factors of MAE method application, the package optimization of MFCS has been investigated. It is found that the best packaging length is from the groove end to the place where the 2 fibers just begin to fuse, which could achieve both, good completeness of acoustic emission signal wavelet transform and relative high sensitivity. Then, the linear source localization has been performed using the optimized MFCS. The arrival time of the acoustic emission signal is decided by the contour threshold of the wavelet transform to avoid the wave reflection from edge and mode conversion. The locating accuracy with different thresholds is analyzed. It indicates that the result agrees with the actual source position better when higher threshold is set. Furthermore, considering noise contamination and the frequency components completeness, the appropriate range of threshold is suggested to the location calculation. Finally, the favorable localization result proves that applying this optimized MFCS to the MAE source location strategy is quite feasible.

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Section: Discussionmentioning

confidence: 99%

Source location using an optimized microfiber coupler sensor based on modal acoustic emission method

Wang

Liu

et al. 2017

Struct Control Health Monit

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“…It is worth noting that due to the reversibility of this technique of creation of artificial damage, testing the developed damage detection method on an in-service structure could be possible [23]. Furthermore, the magnets can be simply moved along the structure to test the efficiency of damage localization methods [24].…”

Section: Experiments and Database Buildingmentioning

confidence: 99%

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

et al. 2019

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This paper concerns the health monitoring of pipelines and tubes. It proposes the k-means clustering algorithm as a simple tool to monitor the integrity of a structure (i.e., detecting defects and assessing their growth). The k-means algorithm is applied on data collected experimentally, by means of an ultrasonic guided waves technique, from healthy and damaged tubes. Damage was created by attaching magnets to a tube. The number of magnets was increased progressively to simulate an increase in the size of the defect and also, a change in its shape. To test the performance of the proposed method for damage detection, a statistical population was created for the healthy state and for each damage step. This was done by adding white Gaussian noise to each acquired signal. To optimize the number of clusters, many algorithms were run, and their results were compared. Then, a semi-supervised based method was proposed to determine an alarm threshold, triggered when a defect becomes critical.

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“…Structural health monitoring (SHM) has been widely applied in a variety of applications over the past several years with a goal of improving reliability, achieving lower cost condition-based maintenance, performing damage prognosis, and eliminating unexpected catastrophic failures [1,2]. Several approaches have been adopted for structural damage detection, such as vibration testing [3], ultrasonic guided wave inspection [4,5], acoustic emission [6,7], thermal imaging [8], etc. In all SHM efforts, the identification of the healthy state of the structure is the very first step that later This is a preprint version.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification

Sarrafi

Mao

Niezrecki

et al. 2018

Journal of Sound and Vibration

201

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Vibration-based Structural Health Monitoring (SHM) techniques are among the most common approaches for structural damage identification. The presence of damage in structures may be identified by monitoring the changes in dynamic behavior subject to external loading, and is typically performed by using experimental modal analysis (EMA) or operational modal analysis (OMA). These tools for SHM normally require a limited number of physically attached transducers (e.g. accelerometers) in order to record the response of the structure for further analysis. Signal conditioners, wires, wireless receivers and a data acquisition system (DAQ) are also typical components of traditional sensing systems used in vibration-based SHM. However, instrumentation of lightweight structures with contact sensors such as accelerometers may induce mass-loading effects, and for large-scale structures, the instrumentation is labor intensive and time consuming. Achieving high spatial measurement resolution for a large-scale structure is not always feasible while working with traditional contact sensors, and there is also the potential for a lack of reliability associated with fixed contact sensors in outliving the life-span of the host structure. Among the state-of-the-art noncontact measurements, digital video cameras are able to rapidly collect high-density spatial information from structures remotely. In this paper, the subtle motions from recorded video (i.e. a sequence of images) are extracted by means of Phase-based Motion Estimation (PME) and the extracted information is used to conduct damage identification on a 2.3-meter long Skystream® wind turbine blade (WTB). The PME and phased-based motion magnification approach estimates the structural motion from the captured sequence of images for both a baseline and damaged test cases on a wind turbine blade. Operational deflection shapes of the test articles are also quantified and compared for the baseline and damaged states. In addition, having proper lighting while working with high-speed cameras can be an issue, therefore image enhancement and contrast manipulation has also been performed to enhance the raw images. Ultimately, the extracted resonant frequencies and operational deflection shapes are used to detect the presence of damage, demonstrating the feasibility of implementing non-contact video measurements to perform realistic structural damage detection.

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Damage localization in metallic plate structures using edge-reflected lamb waves

Cited by 87 publications

References 32 publications

Source location using an optimized microfiber coupler sensor based on modal acoustic emission method

Source location using an optimized microfiber coupler sensor based on modal acoustic emission method

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification

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