Structural health monitoring of multi-spot welded joints using a lead zirconate titanate based active sensing approach

An active sensing approach using piezoceramic induced stress wave is proposed to provide monitoring and early warning for the development of interface debonding damage of precast segmental concrete beams (PSCBs). Three concrete specimens with toothed interfaces were fabricated and bonded with high-strength epoxy resin adhesive to form PSCBs. Smart aggregates (SAs) embedded in concrete specimens are used as actuators and sensors. The PSCBs are subjected to periodic loading with hydraulic jack to test the different degrees of debonding damage. The experimental results of time-domain and frequency-domain analysis clearly show that the amplitude of the signal received by the piezoceramic sensor is reduced when debonding crack occurs. The energy analysis and damage index based on wavelet packet can be used to determine the existence and severity of interface debonding damage in PSCBs. The experimental research validates the feasibility of monitoring the interface debonding damage in PSCBs using SA transducers based on active sensing technique.

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“…ese properties prepare piezoceramic materials for their wide applications in the field of structural health monitoring and damage detection [20].…”

Section: Introductionmentioning

confidence: 99%

Interface Debonding Detection of Precast Segmental Concrete Beams (PSCBs) Using Piezoceramic Transducer‐Based Active Sensing Approach

Liu

Zhang

Yin

et al. 2019

Mathematical Problems in Engineering

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“…When some faults occur in them, it will inevitably affect the work safety and production efficiency [4,5]. Therefore, analyzing the possible fault characteristics of the rolling bearings has an important practical significance [6][7][8]. Modern signal processing methods provide the main technical means of rolling bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

Zhong

Xiao

et al. 2018

Complexity

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Structural health monitoring and fault state identification of key components, such as rolling bearing, located in the mechanical main drive system, have a vital significance. The acquired fault signal of rolling bearing always presents the obvious nonlinear and nonstationary characteristics. Moreover, the concerned features are submerged in strong background noise. To handle this difficulty, a novel fault signal denoising scheme based on improved sparse regularization via convex optimization is proposed to extract the fault feature of rolling bearing. In this paper, the generalized minimax-concave (GMC) penalty is firstly researched to promote the sparsity of signal, which is based on traditional L1-norm and Huber function. It is designed to estimate the sparse solutions more accurately and maintain the convexity of the cost function. Then, the GMC penalty is extended to 1-D first-order total variation (TV) as nonseparability and nonconvex regularizer. Thus, a convex optimization problem, which involves a quadratic data fidelity term and a convex regularization term, is developed in this paper. To accelerate the convergence of the algorithm, it is solved by forward-backward (FB) iterative algorithm and thus the denoised signal can be obtained. In order to demonstrate its performance, the proposed method is illustrated for numerical simulation signal and applied in the feature extraction of the measured rolling bearing vibration signal.

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“…By using SHM technologies, the real-time damage detection of various structures, including concrete structures [2][3][4][5][6], pipeline structures [7][8][9][10][11][12], and steel structures [13][14][15][16][17][18][19], can be investigated in order to provide early warning and hopefully to avoid accidents. In the aerospace industry, several application areas have garnered significant interests.…”

Section: Introductionmentioning

confidence: 99%

Experimental Damage Identification of a Model Reticulated Shell

Hao

et al. 2017

Applied Sciences

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Abstract:The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg-Marquardt algorithm (L-M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

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Structural health monitoring of multi-spot welded joints using a lead zirconate titanate based active sensing approach

Cited by 25 publications

References 31 publications

Interface Debonding Detection of Precast Segmental Concrete Beams (PSCBs) Using Piezoceramic Transducer‐Based Active Sensing Approach

Interface Debonding Detection of Precast Segmental Concrete Beams (PSCBs) Using Piezoceramic Transducer‐Based Active Sensing Approach

A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

Experimental Damage Identification of a Model Reticulated Shell

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