Impact Monitoring of Smart Composite Laminates Using Neural Network and Wavelet Analysis

Sung, Dae-Un; Oh, Junghoon; Kim, Chun-Gon; Hong, Chang Sun

doi:10.1106/n5e7-m37y-3mar-2kfh

Cited by 80 publications

(29 citation statements)

References 11 publications

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“…Gaul et al (2001) applied a Gauss-Newton method to non linear least square optimization to analyze "synthetic" AE signals. Conversely, an alternative approach to model-based methods for the identification of the impact location was the artificial neural network approach (Sung et al, 2000). Despite this method is suitable for complex structure, it cannot provide an optimum solution.…”

Section: Introduction and Statement Of The Problemmentioning

confidence: 99%

Acoustic emission source localization and velocity determination of the fundamental mode A₀using wavelet analysis and a Newton-based optimization technique

Ciampa¹,

Meo²

2010

Smart Mater. Struct.

141

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This paper investigates the development of an in-situ impact detection monitoring system able to identify in real-time the acoustic emission location. The proposed algorithm is based on the differences of stress waves measured by surface bonded piezoelectric transducers. A joint time frequency analysis based on the magnitude of the Continuous Wavelet Transform was used to determine the time of arrivals of the wave packets. A combination of unconstrained optimization technique associated to a local Newton's iterative method was employed to solve a set of non linear equations in order to assess the impact location coordinates and the wave speed. With the proposed approach, the drawbacks of a triangulation method in terms to estimate a priori the group velocity and the need to find the best time-frequency technique for the time of arrival determination were overcome. Moreover, this algorithm proved to be very robust since it was able to converge from almost any guess point and required little computational time. A comparison between the theoretical and experimental results carried out with piezoelectric film (PVDF) and acoustic emission transducers showed that the impact source location and the wave velocity were predicted with reasonable accuracy. In particular, the maximum error in estimation of the impact location was less than 2% and about 1% for the flexural waves velocity.

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Section: Introduction and Statement Of The Problemmentioning

confidence: 99%

Acoustic emission source localization and velocity determination of the fundamental mode A₀using wavelet analysis and a Newton-based optimization technique

Ciampa¹,

Meo²

2010

Smart Mater. Struct.

141

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“…ANNs are a network of mathematical operations between an input and an output that are linked together via weighted connections [23,29,37]. ANNs can create a meta-model mapping given input and output via training using a previously known input and output data set.…”

Section: Impact Localisation Methodsmentioning

confidence: 99%

“…From previous studies [11,18,23,24,25,29,32,38], it was found that ANNs are able to accurately locate the position of an impact using input in the form of impact signal features such as ToA and signal amplitude. However, the accuracy is significantly reduced when there is variation between input features from an impact at a given location that differs from the features for the said location in the original training set.…”

Section: Impact Localisation Methodsmentioning

confidence: 99%

“…As impacts generate signals with a wide bandwidth [23,29,30], high pass filtering is commonly used to remove vibration noise, as it usually has a lower bandwidth limit [5,23,26]. Here, we applied a 700 Hz Butterworth high pass filter to completely remove all components of the previously added vibration noise.…”

Section: Impact Signal and Contact Behaviour From Hard And Soft Immentioning

confidence: 99%

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Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions

Seno

Aliabadi

2019

Sensors

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A parametric investigation of the effect of impactor stiffness as well as environmental and operational conditions on impact contact behaviour and the subsequently generated lamb waves in composite structures is presented. It is shown that differing impactor stiffness generates the most significant changes in contact area and lamb wave characteristics (waveform, frequency, and amplitude). A novel impact localisation method was developed based on the above observations that allows for variations due to differences in impactor stiffness based on modifications of the reference database method and the Akaike Information Criterion (AIC) time of arrival (ToA) picker. The proposed method was compared against a benchmark method based on artificial neural networks (ANNS) and the normalised smoothed envelope threshold (NSET) ToA extraction method. The results indicate that the proposed method had comparable accuracy to the benchmark method for hard impacts under various environmental and operational conditions when trained only using a single hard impact case. However, when tested with soft impacts, the benchmark method had very low accuracy, whilst the proposed method was able to maintain its accuracy at an acceptable level. Thus, the proposed method is capable of detecting the location of impacts of varying stiffness under various environmental and operational conditions using data from only a single impact case, which brings it closer to the application of data driven impact detection systems in real life structures.

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“…Sung et al . used the neural network algorithm to estimate impact locations on a graphite composite plate. Sekine et al .…”

Section: Introductionmentioning

confidence: 99%

Real-time impact identification algorithm for composite structures using fiber Bragg grating sensors

Jang

Lee

Kim

et al. 2012

Struct. Control Health Monit.

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In composite structures, low-velocity impact-induced damage such as delamination are mostly hidden inside laminates or leave a small dent on the impact side. Thus, detecting this type of damage using conventional inspection methods is not easy and requires much time and cost. To enhance efficiency of these methods, information on the estimated impact locations must be provided with a high accuracy. In this way, unnecessary inspections for large intact regions can be reduced. In this study, impact localization algorithms for various composite structures were developed using the impact induced acoustic signals acquired by multiplexed fiber Bragg grating (FBG) sensors. The acoustic waves from a given impact were transmitted to each FBG sensor, and incurred FBG wavelength shifts were captured by a high speed multiplexible FBG interrogation system with a sampling frequency of 100 kHz. After acquisition of the FBG sensor signals at all the training points in the target section, the impact wave arrival time differences between each FBG signal were calculated to produce the input data sets for neural network training. To reliably use the neural network algorithm for impact identification, high reproducible arrival time determination algorithms are essentially required. In this study, such arrival time determination algorithms were developed through various types of structures. Finally, we evaluated the performances of the suggested impact identification algorithms for a composite wing box structure.

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Impact Monitoring of Smart Composite Laminates Using Neural Network and Wavelet Analysis

Cited by 80 publications

References 11 publications

Acoustic emission source localization and velocity determination of the fundamental mode A₀using wavelet analysis and a Newton-based optimization technique

Acoustic emission source localization and velocity determination of the fundamental mode A₀using wavelet analysis and a Newton-based optimization technique

Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions

Real-time impact identification algorithm for composite structures using fiber Bragg grating sensors

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Impact Monitoring of Smart Composite Laminates Using Neural Network and Wavelet Analysis

Cited by 80 publications

References 11 publications

Acoustic emission source localization and velocity determination of the fundamental mode A0using wavelet analysis and a Newton-based optimization technique

Acoustic emission source localization and velocity determination of the fundamental mode A0using wavelet analysis and a Newton-based optimization technique

Impact Localisation in Composite Plates of Different Stiffness Impactors under Simulated Environmental and Operational Conditions

Real-time impact identification algorithm for composite structures using fiber Bragg grating sensors

Contact Info

Product

Resources

About

Acoustic emission source localization and velocity determination of the fundamental mode A₀using wavelet analysis and a Newton-based optimization technique

Acoustic emission source localization and velocity determination of the fundamental mode A₀using wavelet analysis and a Newton-based optimization technique