An ultrasonic signal processing method to improve defect depth estimation in composites based on empirical mode decomposition

Cao, Heng; Jiang, Mingshun; Lei, Jia; Ma, Mengyuan; Sun, Luwei; Zhang, Lei; Tian, Anli; Liang, Jianying

doi:10.1088/1361-6501/ac09b4

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…As such, a multivariate version of the EMD, termed Multivariate EMD (MEMD), was recently proposed, which facilitates the decomposition of multi-channel vibration signals [ 265 , 266 , 267 ]. Cao et al [ 268 ] developed an ultrasonic signal processing method for non-destructive testing of composite structures by improving the depth evaluation of phased array ultrasonic waves. The developed algorithm is based on a combination of EMD, correlation coefficient analysis, a fuzzy entropy algorithm, and Hilbert transform and, as such, can be regarded as an improved adaptive time-frequency analysis algorithm.…”

Section: Advanced Hybrid Vibration Methodsmentioning

confidence: 99%

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani

Mousavi

Gandomi

2021

Sensors

102

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This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

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Section: Advanced Hybrid Vibration Methodsmentioning

confidence: 99%

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani

Mousavi

Gandomi

2021

Sensors

102

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“…Figure 21 is a schematic diagram of an ultrasonic testing system. The ultrasonic testing device includes a transmitting/receiving probe (1), sample material (2), ( 3) and ( 4), water layer (5) and cup (6), second bonding interface (7) and first bonding interface (8), signal amplifier (10), computer (12), transmitter/receiver device (11), and hydraulic oil cylinder (14), rack (13) and upper roof (9), etc.…”

Section: Testing Devicementioning

confidence: 99%

“…To achieve reliable Non-destructive testing (NDT) of the multilayered structure and control the weakened bonding interface, it is crucial to develop reliable NDT methods. With the development of NDT towards automation and intelligence [8]. Currently, ultrasonic valuation method is the most suitable technique for NDT of multilayered structure [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluating interfacial bonding quality of multilayered structure based on ultrasonic testing technology

Wang

et al. 2023

Meas. Sci. Technol.

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An ultrasonic evaluation method of interfacial bonding quality from layered structures is proposed for enhancing the testing accuracy. The ultrasonic reflection and transmission characteristic is theoretically analyzed, which focus on the different quality of the single and double bonding interface with spring-type. The reflection coefficient and transmission coefficient mathematic models, which include multiple parameters, are derived from the transfer matrix. Taking the first, second, and the double bonding interface as the research objects, respectively, two types of resonant frequencies have been presented by numerical solving the model, namely, the first type resonance frequency (RFI) and the second type resonance frequency (RFII). The results show that the interval of the RFⅠ is related to medium thickness of the maximum acoustic impedance. Similarly, the interval of the RFⅡ depends on other layers’ thickness. In addition, the transmission coefficient shows a change in different trends with the bonding interface gradually tending to rigid. When the adhesive layer thickness is sufficiently small compared with the wavelength, the reflection coefficient shows many local minima points at a certain frequencies range, the frequency of local minimum point increases with the increase of the stiffness coefficient. The experimental results are in good agreement with the numerical solution results and finite element analysis (FEA) results.

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“…In this paper, the signal reconstructed by ISWT is selected as In statistical analysis, the correlation coefficient is defined as a measure of the degree of correlation between two vectors. The Pearson correlation coefficient [33][34][35], a commonly used criterion, is an improvement based on Euclidean distance [36] and cosine distance [37], and its output ranges from −1 to 1. Assuming that there are two vectors x = (x 1 , x 2 , .…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

SWT-KELM-based rolling bearing fault diagnosis method under noise conditions with different SNRs

Wang¹,

Ye²,

Jiang³

et al. 2022

Meas. Sci. Technol.

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Most existing studies realize bearing fault diagnosis tasks in labs with weak noise. However, field noise is so heavy under actual conditions that some methods may suffer from degradation or failure. To solve this problem, a fault diagnosis framework is proposed based on synchrosqueezing wavelet transform and kernel extreme learning machine (SWT-KELM). First, vibration signals are collected, and white Gaussian noise is added. Second, SWT is employed for signal decomposition in the time-frequency domain, and inverse SWT (ISWT) is applied for sub-signal reconstruction. Sub-signals with high correlation coefficients are selected for further feature extraction, specifically by singular value decomposition (SVD) to obtain singular values as the fault feature. Third, the KELM model, in which the beetle antennae search algorithm (BAS) is employed for parameters optimization, is constructed to classify the faults. For verification, the proposed method is implemented on the Case Western Reserve University (CWRU) dataset and Lab-625 dataset, and the results show that it maintains satisfactory outcomes on original and noise-contaminated data. Specifically, under noise conditions, the accuracies of the two datasets average at 96% and 83%, respectively, indicating the robustness and generalization of the method compared to other methods.

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An ultrasonic signal processing method to improve defect depth estimation in composites based on empirical mode decomposition

Cited by 18 publications

References 32 publications

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Evaluating interfacial bonding quality of multilayered structure based on ultrasonic testing technology

SWT-KELM-based rolling bearing fault diagnosis method under noise conditions with different SNRs

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