Multiple crack identification in Euler beams using extreme learning machine

Ghadimi, Siamak; Kourehli, Seyed Sina

doi:10.1007/s12205-016-1078-0

Cited by 20 publications

(5 citation statements)

References 19 publications

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“…These dynamic responses contain information regarding the locations and sizes of the crack(s). Many researchers have focused on the vibration characteristics of cracked beams [ 1 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Ostachowicz and Krawczuk (1991) modelled the crack as a torsional spring model and calculated the natural frequencies of single-sided and double-sided crack cantilever beams [ 46 ].…”

Section: Introductionmentioning

confidence: 99%

“…The analytical and experimental results highlighted that the natural frequency of the cracked beam decreased as the crack grew. Several studies have been performed to investigate the dynamic response of a cracked beam by using a spring stiffness model derived based on fracture mechanics [ 8 , 10 , 12 , 13 , 16 , 19 , 20 , 28 , 41 , 47 , 48 , 49 ]. However, the torsional spring model can only represent the shallow cracks.…”

Section: Introductionmentioning

confidence: 99%

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Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Khan

Aldosari

2022

Polymers

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Acrylonitrile butadiene styrene (ABS) is the most commonly used thermoplastic printing material for fused deposition modelling (FDM). FDM ABS can be used in a variety of complex working environments. Notably, the thermo-mechanical coupled loads under complex operating conditions may lead to cracking and ultimately catastrophic structural failure. Therefore, it is crucial to determine the crack depth and location before a structural fracture occurs. As these parameters affect the dynamic response of the structure, in this study, the fundamental frequency and displacement amplitude response of a cracked 3D-printed ABS cantilever beam in a thermal environment were analytically and experimentally investigated. The existing analytical model, specifically the torsional spring model used to calculate the fundamental frequency change to determine the crack depth and location was enhanced by the proposed Khan-He model. The analytical relationship between the displacement amplitude and crack was established in Khan-He model and validated for the first time for FDM ABS. The results show that a reduced crack depth and location farther from the fixed end correspond to a higher fundamental frequency and displacement amplitude. An elevated ambient temperature decreases the global elastic modulus of the cracked beam and results in a lower fundamental frequency. Moreover, a non-monotonic relationship exists between the displacement amplitude and ambient temperature. The displacement amplitude is more sensitive to the crack change than the fundamental frequency in the initial stages of crack growth.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Khan

Aldosari

2022

Polymers

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“…Yu et al [25] studied the motor rotor fault diagnosis based on ELM model by employing the discrete Fourier transform (DFT) to extract fault features. In the study of Ghadimi et al [26] using Euler beam to identify the structures of cracks, the authors employed ELM to learn sensitive features from such parameters as modal strain energy and natural frequency, and detected the structural damage. Shu et al [27] proposed the validity of variance and covariance calculated from the structural dynamic response analysis to train the damage identification model as the input of artificial neural network (ANN), and realized the evaluation of the location and damage condition of bridge structure.…”

Section: Introductionmentioning

confidence: 99%

Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

Li,

Zou,

Luo

et al. 2023

SDHM

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As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine (ELM) model to identify the impact location and damage degree, and the Gray Wolf Optimization (GWO) algorithm is employed to update the weight parameters of the model. Finally, experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm × 500 mm × 10 mm, the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas, respectively. Comparative experiments with ELM and backpropagation neural networks (BPNN) demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.

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“…A large amount of research has applied ML techniques to identify the structural crack severity. Some of them proposed ML based methods for crack identification in beams and plates [21][22][23]. Liu and Zhang [24] employed convolutional neural networks (CNN) for crack damage detection in thin aluminum plates.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation

Fleet

Kamei

et al. 2020

Sensors

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Accurate damage detection in engineering structures is a critical part of structural health monitoring. A variety of non-destructive inspection methods has been employed to detect the presence and severity of the damage. In this research, machine learning (ML) algorithms are used to assess the dynamic response of the system. It can predict the damage severity, damage location, and fundamental behaviour of the system. Fatigue damage data of aluminium and ABS under coupled mechanical loads at different temperatures are used to train the model. The model shows that natural frequency and temperature appear to be the most important predictive features for aluminium. It appears to be dominated by natural frequency and tip amplitude for ABS. The results also show that the position of the crack along the specimen appears to be of little importance for either material, allowing simultaneous prediction of location and damage severity.

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Multiple crack identification in Euler beams using extreme learning machine

Cited by 20 publications

References 19 publications

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Interdependencies between Dynamic Response and Crack Growth in a 3D-Printed Acrylonitrile Butadiene Styrene (ABS) Cantilever Beam under Thermo-Mechanical Loads

Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation

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