Identification of multiple directional damages in a thin cylindrical shell

Lee, Usik; Kim, Sunghwan

doi:10.1016/j.ijsolstr.2005.03.077

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The WT has a certain ability to denoise ultrasonic signals, but needs to go through a complex process to obtain the wavelet threshold and threshold function. Moreover, the WT may distort the original signals, leading to discrete and oscillating signals [2][3][4]. The AF relies on the selection of the reference signal, which is not always correlated with the noise signals [5][6].…”

Section: Introductionmentioning

confidence: 99%

Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

Zhu¹,

Xu²,

Xiao³

2019

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To remove the noise in the echo signals of ultrasonic pulse-echo testing, this paper puts forward a denoising algorithm based on the generalized S transform (GST) and singular value decomposition (SVD). Firstly, the ultrasonic echo signals were subjected to the GST, yielding the time-frequency matrix of the signals. Next, the matrix was taken as the Hankel matrix, and went through the SVD. The threshold for singular values to be zeroed was determined by the ratio between singular entropy increments. After zeroing the singular values representing the noise, the resulting denoised 2D time-frequency matrix was subjected to the inverse GST, generating the denoised echo signals. Our method was applied to denoise the simulated ultrasonic echo signals with different signal-to-noise ratios (SNRs), and compared with the wavelet soft thresholding (WST) method. The comparison show that our method outperformed the WST, especially in denoising the signals with low SNR. In addition, a scanning acoustic microscopy (SAM) system was designed for the experimental verification of our method. The C-scan image with our method was much better than that without our method. Hence, our method was proved feasible and effective.

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Section: Introductionmentioning

confidence: 99%

Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

Zhu¹,

Xu²,

Xiao³

2019

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“…With the extension of the service time of the industrial pipes, the damage caused by material defects, corrosion, or external force, makes the pipeline security situation gradually deteriorated. A lot of nondestructive testing technologies are available to inspect defected pipes [1][2][3]. However, they are time-consuming works because every industrial pipe needs to be tested by placing huge number of sensors for most conventional nondestructive tests.…”

Section: Introductionmentioning

confidence: 99%

2-D Defect Profile Reconstruction from Ultrasonic Guided Wave Signals Based on Radial Wavelet Basis Function Neural Network with ELM

Liu¹,

Tang²,

Yu³

et al. 2016

KEM

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The reconstruction of defect profiles based on ultrasonic guided waves means the acquisition of defect profiles and parameters from ultrasonic guided wave signals. To achieve multi-resolution approximation, this paper proposed a reconstruction approach based on Radial Wavelet Basis Function Neural Network (RWBFNN), which combines wavelet analysis and neural network. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelet functions respectively. The training and testing samples contain simulation data and experimental data. The input data sets are defect echo signals, and the output data sets are 2-D profile parameters. To reduce the training time and simplify the profile reconstruction procedure without losing accuracy, Extreme Learning Machine (ELM) is adopted simultaneously. The results indicate that significant advantages can be obtained over other defect profile reconstruction schemes, and the accuracy of the predicted defect profile can be controlled by the resolution of the network with the lower computational complexity.

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“…Ip and Tse [24] proposed an approach based on frequency sensitivities and mode shapes; Sarker et al [25] also demonstrated that the curvature of mode shapes was sensitive to local damage. Lee et al [26][27] illustrated that multiple directional damages can be identified by using Frequency Response Functions (FRFs). Other researchers developed several promising approaches based on advanced dynamic properties.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Compressed Natural Gas (CNG) Cylinders Based on Auxiliary Mass Induced Frequency Shift

2014

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This paper proposes a new damage detection method for cylindrical shell structures based on the frequency shift induced by an auxiliary mass. The natural frequency of a cylindrical shell changes when an auxiliary mass is placed at different positions on its surface. In fact, when the auxiliary mass is approaching the damage resulting in a decrease of local stiffness, the natural frequencies will drop drastically. This is because the auxiliary mass increases the local mass. That is, the auxiliary mass can enhance the influence of the damage on the dynamic characteristics of the cylindrical shell, and therefore, it can be used to probe the damage by traversing it over the entire surface of the cylindrical shell. On the other hand, the sudden drop of natural frequencies induced by the auxiliary mass approaching the damage can be considered as an anomaly since the frequencies usually change smoothly under the assumption that intact cylindrical shell is homogeneous and smooth. To detect the anomaly, a new damage index is proposed based on the curvature of frequency shift, which only uses the information from the damaged cylindrical shell. The proposed method is then tested on a damaged CNG cylinder; it demonstrates that it is more accurate and sensitive compared to other traditional vibration based damage detection methods which depend on mode shape instead.

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Identification of multiple directional damages in a thin cylindrical shell

Cited by 10 publications

References 16 publications

Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

2-D Defect Profile Reconstruction from Ultrasonic Guided Wave Signals Based on Radial Wavelet Basis Function Neural Network with ELM

Damage Detection in Compressed Natural Gas (CNG) Cylinders Based on Auxiliary Mass Induced Frequency Shift

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