Determining impact induced damage by lamb wave mode extracted by EMD method

Xu, Bo; Yuan, Shenfang; Wang, Mulan; Qiu, Lei

doi:10.1016/j.measurement.2014.11.033

Cited by 21 publications

(8 citation statements)

References 13 publications

(18 reference statements)

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“…This pitch–catch configuration reduced the isolated region and improved the resolution in the damage monitoring area [17]. Also, four PZT discs have been arranged on the wing slat of Boeing 737 aircraft [18], and three PZT sensors have been attached on the surface of a composite plate [19] to excite and sense the Lamb waves.…”

Section: Introductionmentioning

confidence: 99%

“…Discrete wavelets and the Hilbert transform have been combined to process Lamb wave signals [13], and to estimate the damage location and size using arrival time and amplitude of the damage-scattered peak [18]. Empirical mode decomposition (EMD) has been used to decompose the scattered signal in a damaged plate to obtain the damage size and location [19]. Wavelet transforms have been used in dispersion analysis of Lamb waves and detection of damage in aluminum plates [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lamb-Wave-Based Multistage Damage Detection Method Using an Active PZT Sensor Network for Large Structures

Hameed

Chen

et al. 2019

Sensors

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A multistage damage detection method is introduced in this work that uses piezoelectric lead zirconate titanate (PZT) transducers to excite/sense the Lamb wave signals. A continuous wavelet transformation (CWT), based on the Gabor wavelet, is applied to accurately process the complicated wave signals caused by the damage. For a network of transducers, the damage can be detected in one detection cell based on the signals scattered by the damage, and then it can be quantitatively estimated by three detection stages using the outer tangent circle and least-squares methods. First, a single-stage damage detection method is carried out by exciting a transducer at the center of the detection cell to locate the damaged subcell. Then, the corner transducers are excited in the second and third stages of detection to improve the damage detection, especially the size estimation. The method does not require any baseline signal, and it only utilizes the same arrangement of transducers and the same data processing technique in all stages. The results from previous detection stages contribute to the improvement of damage detection in the subsequent stages. Both numerical simulation and experimental evaluation were used to verify that the method can accurately quantify the damage location and size. It was also found that the size of the detection cell plays a vital role in the accuracy of the results in this Lamb-wave-based multistage damage detection method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lamb-Wave-Based Multistage Damage Detection Method Using an Active PZT Sensor Network for Large Structures

Hameed

Chen

et al. 2019

Sensors

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“…The Lamb waves-based SHM is considered one of the promising techniques because of its tremendous sensitivity to various defects, and its high detection efficiency. [27][28][29] Lamb waves are generated using the thermal-elastic effect, with an elevated signal-to-noise ratio (SNR). However, high-dimensional wave-field data requires more advanced interpretation to reveal wave propagation frameworks and fault features.…”

Section: Introductionmentioning

confidence: 99%

Damage assessment of laminated composites using unsupervised autonomous features

Khan,

Kim

2023

Journal of Thermoplastic Composite Materials

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This article proposes a framework for the damage assessment of and effect of temperature variations in laminated composites using Lamb waves and unsupervised autonomous features. A network of piezoelectric transducers is employed to generate data for 18 health states of a laminated composite plate. The data is processed with sparse autoencoder (SAE) for unsupervised autonomous features. The discriminative capabilities of the extracted features are confirmed by processing the feature space in the supervised and unsupervised frameworks of machine learning. The confusion matrices of supervised learning provided physical insights into the problem. The feature space was also visualized in two dimensions in an unsupervised manner through principal component analysis (PCA), which revealed physically consistent results for the effect of temperature variations, damage of different severity levels, and the undamaged paths between the actuator and sensors. The healthy state data and information on the paths between the actuator and sensors was processed via SAE for damage localization. The proposed approach can be employed for the autonomous assessment of composite structures for the presence of damage and variations of operating temperatures while using both supervised and unsupervised machine learning algorithms.

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“…In the last decades, with the help of the rapid development and successful applications of time series analysis methods, many signal processing technologies, including filtering [21], correlation analysis [22], fast Fourier transform [23,24], shorttime Fourier transform [25,26], empirical mode decomposition (EMD) [27,28], ensemble EMD [29], variational mode decomposition (VMD) [30] and other methods [31][32][33][34][35][36], are implemented to enhance or extract the feature of the damage from the complex signals in Lamb wave-based SHMs. Moreover, some researches recently indicate that the singularities of the signal are related to some features of the structural damage, and these singularities are usually obtained by singular spectrum analysis (SSA).…”

Section: Introductionmentioning

confidence: 99%

Singular spectrum analysis and fuzzy entropy-based damage detection on a thin aluminium plate by using PZTs

Song

Ming

et al. 2022

Smart Mater. Struct.

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In this research, a new method based on singular spectrum analysis (SSA) and fuzzy entropy is developed for damage detection on thin wall-like structures, and the normalized fuzzy entropy is employed as an indicator to identify the severity of the damage. The Lead Zirconate Titanate (PZT) transducers are used in this research to generate and detect the Lamb waves. During the detection, the collected signals from the PZT sensors are firstly decomposed and reconstructed by SSA to extract the feature of the damage, and then the reconstructed signals with the feature of the damage are processed to obtain the normalized fuzzy entropy. An experimental setup of an aluminium plate with added magnets is fabricated to validate the proposed method. The experimental results show that when magnets are attached on the aluminium plate, the normalized fuzzy entropy is smaller than that when there are no magnets. That is because when magnets are placed on the plate, the movement and some vibration modes of Lamb waves are disturbed by the added magnets and this disturbing effect can be enhanced by increasing the number and locations of the added magnets, and eventually the complexity and nonlinearity of the waves are weakened. The experimental results of a single damage with different number of magnets indicate that the normalized fuzzy entropy decreases linearly as the number of the added magnets increases, which demonstrates that the proposed method can be used to detect the severity of the damage. Moreover, the experimental results of multi-damage on different locations indicate that the normalized fuzzy entropy is relevant with both the total number and locations of the added magnets. The normalized fuzzy entropy decreases linearly as the total number of the magnets increases, and the entropy of a single damage is smaller than that of the multi-damage with the same total number of magnets, which demonstrates that the proposed method also can be used for multi-damage detection on a thin plate. This study provides us a new approach to identifying a single or multiple damages on thin wall-like structures.

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Determining impact induced damage by lamb wave mode extracted by EMD method

Cited by 21 publications

References 13 publications

Lamb-Wave-Based Multistage Damage Detection Method Using an Active PZT Sensor Network for Large Structures

Lamb-Wave-Based Multistage Damage Detection Method Using an Active PZT Sensor Network for Large Structures

Damage assessment of laminated composites using unsupervised autonomous features

Singular spectrum analysis and fuzzy entropy-based damage detection on a thin aluminium plate by using PZTs

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