Nonlinear Lamb wave mixing for assessing localized deformation during creep

Metya, Avijit Kr.; Tarafder, S.; Balasubramaniam, Krishnan

doi:10.1016/j.ndteint.2018.04.013

Cited by 57 publications

(15 citation statements)

References 19 publications

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“…Given the multimodal nature of guided waves, and the existence of higher-order propagation modes, limited work in the field of nonlinear guided-wave mixing can be found in the literature, aiming at fundamentally understanding the mixing phenomenon as in [ 32 , 33 , 34 , 35 ] and some types of damage-related mechanisms [ 36 , 37 , 38 ]. Croxford et al [ 39 ] investigated material degradation detection using wave mixing, and Jingpin et al investigated the use of wave mixing to detect fatigue crack [ 40 ] and thermal corrosion [ 41 ] damage in steel specimens.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Guided-Wave Mixing for Condition Monitoring of Bolted Joints

Allen

2021

Sensors

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Bolted joints are fundamental to numerous structural components in engineering practice. Nevertheless, their failure or even their loosening can lead to insufficient performance and reduced structural safety. This study presents a theoretical development and experimental investigation into nonlinear guided-wave mixing for integrity monitoring of bolted joints in plates. Combinational harmonics generated due to nonlinear Lamb wave mixing and contact acoustic nonlinearity at the bolted joints were used to evaluate the applied torque level in the joint. The area of the power spectral density in the region of the sum combinational harmonic bandwidth is found to be highly correlated to the applied torque level at the joint. Moreover, the effect of the number of cycles and thus the time duration of the excitation is investigated. The results show that the combinational harmonics remain robust for different numbers of cycles in detecting bolt loosening. The findings presented in this study also provide physical insight into the phenomena of nonlinear Lamb wave mixing for evaluating applied torque in bolted joints, and the results help further advance the use of nonlinear guided waves for damage detection.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Guided-Wave Mixing for Condition Monitoring of Bolted Joints

Allen

2021

Sensors

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“…A number of defect detection techniques based on non-linear behavior can be defined according to the particular aspect that is considered, among the features resulting from the non-linear characteristic of the examined system; one can distinguish between methods that detect the presence of higher order harmonics [6][7][8], methods that analyze the frequency shift of resonances [9,10], vibro-acoustic modulation methods [5,11], and frequency mixing methods, which highlight the production of non-linear combinations of harmonic components that have been generated due to different phenomena or propagation modes [12,13]. In the first case, the response is analyzed to detect the presence of higher order harmonics: e.g., in [6], it is shown how non-linearity due to the presence of damages manifests itself as sideband components in the spectrum of the received signal, while in [8], a technique based on pulse compression and the scalar subtraction method are compared under the same experimental conditions to highlight their respective sensitivities: a laboratory test experiment on mortar samples is performed to compare their ability to detect spectral components due to early damage in samples showing a non-linear response.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Defects through the Identification of Distortion Characteristics in Ultrasonic Responses

Burrascano¹,

Ciuffetti²

2021

Mathematics

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Ultrasonic techniques are widely used for the detection of defects in solid structures. They are mainly based on estimating the impulse response of the system and most often refer to linear models. High-stress conditions of the structures may reveal non-linear aspects of their behavior caused by even small defects due to ageing or previous severe loading: consequently, models suitable to identify the existence of a non-linear input-output characteristic of the system allow to improve the sensitivity of the detection procedure, making it possible to observe the onset of fatigue-induced cracks and/or defects by highlighting the early stages of their formation. This paper starts from an analysis of the characteristics of a damage index that has proved effective for the early detection of defects based on their non-linear behavior: it is based on the Hammerstein model of the non-linear physical system. The availability of this mathematical model makes it possible to derive from it a number of different global parameters, all of which are suitable for highlighting the onset of defects in the structure under examination, but whose characteristics can be very different from each other. In this work, an original damage index based on the same Hammerstein model is proposed. We report the results of several experiments showing that our proposed damage index has a much higher sensitivity even for small defects. Moreover, extensive tests conducted in the presence of different levels of additive noise show that the new proposed estimator adds to this sensitivity feature a better estimation stability in the presence of additive noise.

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“…Wang 16 proposed an improved virtual time reversal to eliminate the transducer influence. Metya 17 used nonlinear excitation to analyze the localized deformation. These methods can transform the original signals into obvious diagnostic information.…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning‐based guided wave detection for liquid‐level state in porcelain bushing type terminal

Hong

Zhang

Liu

et al. 2020

Struct Control Health Monit

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The structural security of civil energy equipment is significant for the steady operation of power supply system, and porcelain bushing type terminal is a typical energy equipment that needs long-term monitoring. As a nondestructive structural health monitoring method, ultrasonic guided wave (UGW) technology is extremely suitable for state detection of energy equipment. However, most current UGW methods still need to manually select the guided wave features, which rely heavily on the guidance of expert experience. This article presents a deep-learning method to directly utilize original-guided wave signals to quantitatively detect the liquid-level state. Firstly, the original signals were fed into convolutional autoencoder (CAE) to catch the low-dimension representation and realize the automatic feature extraction. Then, the low-dimension representations were orderly input into the long short-term memory (LSTM) recurrent neural network for liquidlevel regression. In feature extraction step, CAE method can effectively extract the useful features and remove the interference and signal distortion. In regression step, both the accuracy and the robustness of proposed method are better than backpropagation network and convolutional neural network. Experimental results show that proposed CAE-LSTM method can accurately inspect the liquid level by original signals and implement maintenance monitoring.

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Nonlinear Lamb wave mixing for assessing localized deformation during creep

Cited by 57 publications

References 19 publications

Nonlinear Guided-Wave Mixing for Condition Monitoring of Bolted Joints

Nonlinear Guided-Wave Mixing for Condition Monitoring of Bolted Joints

Early Detection of Defects through the Identification of Distortion Characteristics in Ultrasonic Responses

Deep‐learning‐based guided wave detection for liquid‐level state in porcelain bushing type terminal

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