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Journal ArticleDOI: 10.1080/09243046.2020.1786903

Acoustic emission source localization in composite stiffened plate using triangulation method with signal magnitudes and arrival times

04 Mar 2021-Advanced Composite Materials (Taylor & Francis)-Vol. 30, Iss: 2, pp 149-163
Abstract: This paper introduced the triangulation method using the relations of the signal magnitudes and distances between each sensor and impact location. In order to extract the useful feature having good...

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Topics: Triangulation (social science) (57%), Signal (51%)
Citations
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9 results found


Open accessDissertation
01 Jan 2001-
Abstract: Low-velocity impact damage is a major concern in the design of structures made of advanced laminated composites, because such damage is mostly hidden inside the laminates and cannot be detected by visual inspection. It is necessary to develop the impact monitoring techniques providing on-line diagnostics of smart composite structures susceptible to impacts. In this paper, we discuss the process for impact location detection in which the generated acoustic signals are detected by PZT using the improved neural network paradigms. To improve the accuracy and reliability of a neural network based impact identification method, the Levenberg-Marquardt algorithm and the generalization methods were applied. This study concentrates not only on the determination of the location of impacts from sensor data, but also the implementation of time-frequency analysis such as the Wavelet Transform (WT) to measure the characteristic frequencies of acoustic emission waves for the determination of the occurrence and the estimation of impact damage.

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77 Citations


Journal ArticleDOI: 10.1080/09243046.2020.1802805
Abstract: Recently, carbon fiber reinforced thermoplastics (CFRTP) have been widely used for various applications instead of carbon fiber reinforced thermosetting plastics (CFRP). Some matrix resins of CFRTP...

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4 Citations


Journal ArticleDOI: 10.1016/J.YMSSP.2020.107547
Qi Liu1, Fengde Wang2, Jindong Li1, Wensheng Xiao1Institutions (2)
Abstract: The accurate localization of low-velocity impacts on the composite plate structure of the ship is still a great challenge. Current research mainly focuses on extracting single domain features from impact signals as the input of machine learning methods, whereas ignores multi-domain features with more comprehensive impact information. In this paper, a hybrid support vector regression with multi-domain features is proposed to increase the localization accuracy in determining the locations of low-velocity impacts on the composite plate structure. The proposed method consists of the signal preprocessing, the multi-domain feature extraction, and the impact localization. In the signal preprocessing, the trend component in the low-velocity impact signals is eliminated by adopting the empirical mode decomposition (EMD) method. Then, the multi-domain features, which include time domain features, frequency domain features, and time-frequency domain features, are extracted from the preprocessed impact signals. Finally, the optimized support vector regression based on the bat algorithm (BA-SVR) is designed to implement the localization of low-velocity impacts. The low-velocity impact localization system using four fiber Bragg grating (FBG) sensors is established on a carbon fiber reinforced plastic (CFRP) plate, and then five sets of experiments are executed. The statistical results in these experiments demonstrate the effectiveness and feasibility of BA-SVR that uses multi-domain features and four FBG sensors and the satisfactory localization performance of the proposed method in handling the low-velocity impact localization problem on the CFRP plate.

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Topics: Frequency domain (54%), Time domain (53%), Composite plate (53%) ... read more

3 Citations


Open accessJournal ArticleDOI: 10.1016/J.IJHYDENE.2021.10.262
Jun-Min Lee1, Yunshil Choi1, Jung-Ryul Lee1Institutions (1)
Abstract: Acoustic emission (AE) is present when transient elastic waves from structures are generated by various causes, such as structural cracks, fiber breakage, debonding of fibers and matrix, temperature changes, and fatigue. In AE-based structural health monitoring, the simple event counting method is unable to determine where AE occurs, so it is possible to discard a structure even if it is safe or not discard it when it is not safe. Much research on AE localization has been conducted to solve these problems. However, most of the methods have limitations with respect to isotropic material or near field conditions and cannot be applied when there is a change in the boundary conditions of the structure or obstacles. Thus, to solve these problems, a Q-switched laser capable of generating elastic waves has been used to scan and train the structures. Although this method worked effectively on thin specimens, a more advanced method is required for thick and complex structures, such as a fuel tank of a fuel cell electric vehicle (FCEV). Therefore, we propose a novel method based on artificial intelligence (AI) that can be applied to a real FCEV fuel tank fabricated with a filament winding composite. More specifically, this technique modulates the difference in characteristics between AE and laser-induced elastic waves in the frequency domain with AI. AE is simulated by a pencil lead break of the Hsu-Nielsen source. Then, AE localization is performed through cross-correlation in the time⋅frequency domains between a generated AE signal and modulated laser-induced signals obtained from AI. In addition, an experiment conducted to localize the AE that occurs at arbitrary points in real time confirms that AE localization can be performed within 2 s. Finally, an AI algorithm is proposed to distinguish between structural AE and unwanted noise to consider real-world applications and visualize the features of these two types of signals.

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Journal ArticleDOI: 10.1080/09243046.2021.1950277
Abstract: Acoustic emission (AE) measurement has been used to investigate microscopic failures and damage progress in carbon fiber reinforced plastics (CFRP). In this work, AE measurements were conducted on ...

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References
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27 results found


Journal ArticleDOI: 10.1016/0029-1021(76)90027-X
A. Tobias1Institutions (1)
Abstract: Sensors on the surface of a material under stress can detect acoustic emissions from a defect within the material. The difference in time of detection of an emission from the defect at different sensors gives a way of finding where it is. This paper shows a general method of calculating the location of defects in two dimensions from the arrival times at the sensors. The ACEMAN system, which uses this method can derive the resolution properties of a sensor array in about 7.5 min.

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Topics: Sensor array (60%), Acoustic emission (52%)

223 Citations


Journal ArticleDOI: 10.1016/J.JSV.2009.08.037
Abstract: This paper introduces a novel method of acoustic emission (AE) analysis which is particularly suited for field applications on large plate-like reinforced concrete structures, such as walls and bridge decks. Similar to phased-array signal processing techniques developed for other non-destructive evaluation methods, this technique adapts beamforming tools developed for passive sonar and seismological applications for use in AE source localization and signal discrimination analyses. Instead of relying on the relatively weak P-wave, this method uses the energy-rich Rayleigh wave and requires only a small array of 4–8 sensors. Tests on an in-service reinforced concrete structure demonstrate that the azimuth of an artificial AE source can be determined via this method for sources located up to 3.8 m from the sensor array, even when the P-wave is undetectable. The beamforming array geometry also allows additional signal processing tools to be implemented, such as the VESPA process (VElocity SPectral Analysis), whereby the arrivals of different wave phases are identified by their apparent velocity of propagation. Beamforming AE can reduce sampling rate and time synchronization requirements between spatially distant sensors which in turn facilitates the use of wireless sensor networks for this application.

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Topics: Sensor array (64%), Beamforming (63%), Acoustic emission (54%) ... read more

121 Citations


Journal ArticleDOI: 10.1177/1045389X14557506
Zhenhua Tian1, Lingyu Yu1, Cara A. C. Leckey2Institutions (2)
Abstract: Laminated composites are susceptible to delamination due to their weak transverse tensile and interlaminar shear strengths as compared to their in-plane properties. Delamination damage can occur in...

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Topics: Delamination (67%), Lamb waves (55%)

87 Citations


Open accessJournal ArticleDOI: 10.1016/J.COMPOSITESB.2015.09.011
Abstract: Acoustic emission (AE) and infrared thermography (IT) are simultaneously combined to identify damage evolution in carbon fibre reinforced composites. Samples are subjected to tensile static loads while acoustic emission sensors and an infrared camera record the acoustic signals and the temperature variations respectively. Unsupervised pattern recognition procedure is applied to identify damage mechanisms from acoustic signals. Thermodynamic arguments are introduced to estimate global heat source fields from thermal measurements and anisotropic heat conduction behavior is taken into account by means of homogenization technique. A spatial and time analysis of acoustic events and heat sources is developed and some correlation range in the AE and IT events amplitude are identified.

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Topics: Acoustic emission (62%), Thermography (57%)

80 Citations


Open accessDissertation
01 Jan 2001-
Abstract: Low-velocity impact damage is a major concern in the design of structures made of advanced laminated composites, because such damage is mostly hidden inside the laminates and cannot be detected by visual inspection. It is necessary to develop the impact monitoring techniques providing on-line diagnostics of smart composite structures susceptible to impacts. In this paper, we discuss the process for impact location detection in which the generated acoustic signals are detected by PZT using the improved neural network paradigms. To improve the accuracy and reliability of a neural network based impact identification method, the Levenberg-Marquardt algorithm and the generalization methods were applied. This study concentrates not only on the determination of the location of impacts from sensor data, but also the implementation of time-frequency analysis such as the Wavelet Transform (WT) to measure the characteristic frequencies of acoustic emission waves for the determination of the occurrence and the estimation of impact damage.

... read more

77 Citations