Vibration-based damage detection for composite structures using wavelet transform and neural network identification

Graphical abstract2 Highlights  ANNs-solved vibration based parametric identification studies are reviewed. Factors which affect identification result are discussed. Pros and cons of ANN approaches are mentioned. 1• Fundamentals of ANNs. 2• Dynamic behavior analysis for parametric identifications.• Reason for adopting ANNs to vibrational inverse identifications. 3• Earlier ANN approaches to different vibrational parametric identifications.--Signal pre-processing techniques --Input-output schemes --ANN models 4• Factors that affect ANNs performance for vibrational paramteric identification. 5• Advantages and disadvantages of ANN approaches. 6• Suggestions to potential researchers.• Experimental validation of authors' suggestion based on the literature.3  Suggestions are given to potential researchers based on the discussion. Analysis with experimental results is provided to justify some point of view. AbstractVibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs' prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods. Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes.

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“…[65,[122][123][124] used 478, 282, 300, and 108 training data respectively due to moderate relationship between input and output, whereas Ref.…”

Section: Quantity Of Training Samplesmentioning

confidence: 99%

Artificial neural networks for vibration based inverse parametric identifications: A review

Hossain

Ong

Ismail

et al. 2017

Applied Soft Computing

102

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“…NN are mathematical structures enabling the processing of signals by other elements due to the use of certain models which perform input operations. Nowadays we can observe a significant role played by artificial intelligence methods in predicting material parameters or investigating strength of composite materials or failure behavior analyses [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The research on stiffened, buckling-susceptible composite elements used in the aircraft industry, oriented at reducing structure weight, by the finite element method in the Abaqus program and neural networks, was conducted among others by a research team from the Department of Aerospace Science and Technology, Politecnico di Milano [21].…”

Section: Motivationmentioning

confidence: 99%

Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

Golewski

Gajewski

Sadowski

2017

Archives of Metallurgy and Materials

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Artificial neural networks [ANNs] are an effective method for predicting and classifying variables. This article presents the application of an integrated system based on artificial neural networks and calculations by the finite element method [FEM] for the optimization of geometry of a thin-walled element of an air structure. To ensure optimal structure, the structure's geometry was modified by creating side holes and ribs, also with holes. The main criterion of optimization was to reduce the structure's weight at the lowest possible deformation of the tested object. The numerical tests concerned a fragment of an elevator used in the "Bryza" aircraft. The tests were conducted for networks with radial basis functions [RBF] and multilayer perceptrons [MLP]. The calculations described in the paper are an attempt at testing the FEM -ANN system with respect to design optimization.

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“…They used a laser displacement meter for the vibration measurement. In the same year, Yam et al [15] classified the damage location and they identified its severity in composite structures using the vibration responses. They used a piezoelectric patch actuator and sensors for the experiment.…”

Section: Introductionmentioning

confidence: 99%

Multi-Functional Measurement Using a Single FBG Sensor

Mizutani

Groves

2011

Exp Mech

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This paper describes the measurement of average strain, strain distribution and vibration of a cantilever beam made of Carbon Fiber Reinforced Plastics (CFRP), using a single Fibre Bragg Grating (FBG) sensor mounted on the beam surface. Average strain is determined from the displacement of the peak wavelength of reflected spectrum from the FBG sensor. Two unstrained reference FBG sensors were used to compensate for temperature drift. Measured strains agree with those measured by a resistance foil strain gauge attached to the sample. Stress distributions are measured by monitoring the variation in the full width at half maximum (FWHM) values of the reflected spectrum, using a proposed optical analytical model, described in the paper. FWHM values were measured for both the cantilever test beam and for a reference beam, loaded using a four-point bending rig. The trend of the stress distribution for the test beam matches with our analytical model, however with a relatively large noise present in the experimentally determined data. The vibration of a cantilever beam was measured by temporal analysis of the peak reflection wavelength. This technique is very stable as measurements are not affected by variations in the signal amplitude. Finally an application of FBG sensors for damage detection of CFRP plates, by measuring the natural frequency, is demonstrated. With small defects of different sizes applied to the CFRP plate, the natural frequency decreased with damage size.

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Vibration-based damage detection for composite structures using wavelet transform and neural network identification

Cited by 190 publications

References 8 publications

Artificial neural networks for vibration based inverse parametric identifications: A review

Artificial neural networks for vibration based inverse parametric identifications: A review

Optimization of a thin-walled element geometry using a system integrating neural networks and finite element method

Multi-Functional Measurement Using a Single FBG Sensor

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