An effective crack identification method in viscoelastic media using an inverse meshfree method

2021 ◽  
pp. 106834
Author(s):  
Mohammad Hamidpour ◽  
Mohammad Rahim Nami ◽  
Amir Khosravifard
Keyword(s):  
Meshfree Method ◽  
Crack Identification ◽  
Viscoelastic Media ◽  
Identification Method

Effect of a breathing crack on the damping changes in nonlinear vibrations of a cracked beam: Experimental and theoretical investigations

2020 ◽  
pp. 107754632096031
Author(s):  
Masoud Kharazan ◽  
Saied Irani ◽  
Mohammad Ali Noorian ◽  
Mohammad Reza Salimi
Keyword(s):  
Nonlinear Vibrations ◽  
Crack Depth ◽  
Beam Theory ◽  
Experimental Tests ◽  
Damping Coefficient ◽  
Crack Identification ◽  
Breathing Crack ◽  
Cracked Beam ◽  
Identification Method ◽  
Nonlinear Crack

The attempts to identify damping changes in a cracked beam can improve the accuracy of the nonlinear crack identification method. For the purpose of this aim, a parametric nonlinear equation of motion is obtained using the Euler–Bernoulli beam theory and parametric nonlinear breathing crack assumptions. Several experiments were conducted to identify the effect of breathing cracks on changing the damping value in nonlinear vibrations of a cracked beam. Experimental tests have revealed that increasing the crack depth and the level of excitation enlarges the damping coefficient in a vibrating beam. For this reason, the effects of the excitation force and crack depth on the structural damping coefficient are investigated. The obtained results indicated that considering the nonlinear response of a cracked beam and measuring the value of the damping changes can significantly improve the accuracy of the nonlinear crack identification method.

scholarly journals A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hakan Gökdağ
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Particle Swarm ◽  
Dynamic Responses ◽  
Crack Identification ◽  
Discrete Wavelet ◽  
Swarm Optimization ◽  
Coupled Equations ◽  
Identification Method ◽  
Bridge Type

In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO), is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.

Crack Position and Depth Identification in Rotating Shafts: Part 2 — Experimental Results

2005 ◽  
Author(s):  
Nicolo` Bachschmid ◽  
Paolo Pennacchi ◽  
Ezio Tanzi
Keyword(s):  
Dynamical Behavior ◽  
Industrial Applications ◽  
Experimental Results ◽  
Crack Identification ◽  
Identification Method ◽  
Different Types ◽  
Rotating Shafts ◽  
Short Time ◽  
Crack Position ◽  
Deep Crack

This paper presents the experimental validation of a model based transverse crack identification method suitable for industrial machines, described in part 1. The method is validated by experimental results obtained on two test rigs, which were expressly designed for investigating the dynamical behavior of cracked horizontal rotors. On the first test rig, only one crack type is considered, while on the second one three different types of crack have been analyzed: the first is a slot, therefore not actually a crack since it has not the typical breathing behavior, the second a small crack (14% of the diameter) and the third a deep crack (47% of the diameter). The excellent accuracy obtained in identifying position and depth of different cracks proves the effectiveness and reliability of the proposed method. Moreover, the implementation of identification method operates on a PC and takes short time to run, therefore is suitable for industrial applications.

Crack Position and Depth Identification in Rotating Shafts: Part 1— Theoretical Model

2005 ◽  
Author(s):  
Nicolo` Bachschmid ◽  
Paolo Pennacchi ◽  
Andrea Vania
Keyword(s):  
Finite Element ◽  
Transverse Crack ◽  
Crack Identification ◽  
Finite Element Models ◽  
Equivalent System ◽  
Identification Method ◽  
Rotating Shafts ◽  
Breathing Mechanism ◽  
Crack Position ◽  
Relative Theory

This paper presents a model based transverse crack identification method in rotating shafts suitable for industrial machines. The identification method and the relative theory is fully discussed, by presenting the breathing mechanism of the crack, the equivalent system of forces, which excites the vibrations of rotating shafts, the way to localize the crack position along the shaft and finally to identify the depth of the crack. This is obtained by using standard finite element models of the machine and the vibration measured in the bearings only, as usual in industrial machines. The proposed method will be validated by means of experimental results which are described in detail in the second part of the paper.

scholarly journals A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Wenting Qiao ◽  
Hongwei Zhang ◽  
Fei Zhu ◽  
Qiande Wu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Concrete Beams ◽  
Concrete Structures ◽  
Reinforced Concrete Beams ◽  
Bending Test ◽  
Crack Identification ◽  
Identification Method ◽  
Proposed Model

The traditional method for detecting cracks in concrete bridges has the disadvantages of low accuracy and weak robustness. Combined with the crack digital image data obtained from bending test of reinforced concrete beams, a crack identification method for concrete structures based on improved U-net convolutional neural networks is proposed to improve the accuracy of crack identification in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained using the data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling module are added in the improved U-net model. Finally, the widths of cracks are identified using the concrete crack binary images obtained from the improved U-net model. The average precision of the test set of the proposed model is 11.7% higher than that of the U-net neural network segmentation model. The average relative error of the crack width of the proposed model is 13.2%, which is 18.6% less than that measured by using the ACTIS system. The results indicate that the proposed method is accurate, robust, and suitable for crack identification in concrete structures.

scholarly journals Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization

2018 ◽  
Vol 346 (2) ◽  
pp. 110-120 ◽  
Author(s):  
Samir Khatir ◽  
Kevin Dekemele ◽  
Mia Loccufier ◽  
Tawfiq Khatir ◽  
Magd Abdel Wahab
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Crack Identification ◽  
Swarm Optimization ◽  
Identification Method
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