Sensitivity Enhancement of Piezoelectric Transducers for Impedance-Based Damage Detection via a Negative Capacitance Interface

Budoya, Danilo Ecidir; Campeiro, Leandro Melo; Baptista, Fabrício Guimarães

doi:10.1109/jsen.2019.2956782

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…The electromechanical impedance (EMI) technique based on piezoelectric ceramic wafers (PZT) is commonly used for structural health monitoring [4][5][6]. In this technique, piezoelectric sensors are pasted on the structure.…”

Section: Introductionmentioning

confidence: 99%

Electromechanical Impedance Temperature Compensation and Bolt Loosening Monitoring Based on Modified Unet and Multitask Learning

et al. 2023

IEEE Sensors J.

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Bolts are frequently subjected to loosening due to time varying external loads during service. The electromechanical impedance (EMI) technique based on piezoelectric ceramic wafers (PZT) is sensitive to the initial bolt preload looseness. However, the change in environmental temperature has a great effect on EMI monitoring. Deep convolutional neural network (CNN) is a promising technique for EMI monitoring. Nevertheless, it is difficult to train a deep CNN with limited training data to accurately identify damages under a wide range of temperature variations. To this end, this study proposes a multitask CNN for identifying bolts loosening. The network consists of a temperature compensation subnetwork to compensate for the temperature effect, and a lightweight damage identification subnetwork to identify bolt loosening states. The temperature compensation subnetwork is a modified Unet, and both the impedance and temperature are used as its input. The damage identification subnetwork is connected in series behind the temperature compensation subnetwork. A multiloss function is proposed in which a TV regularizer is used. Experimental results show that the validation accuracy of the multitask network is 97.71% when the network is trained by only about 30 samples from each loosening state. Moreover, the generalization abilities of the proposed multitask model to unexpected temperatures and bolt torques are investigated. The model is interpreted by the integrated gradients method, and is also compared with single-task damage identification CNNs. It is proved that the multitask network trained by limited samples can achieve accurate damage identification in temperature varying environments.

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Section: Introductionmentioning

confidence: 99%

Electromechanical Impedance Temperature Compensation and Bolt Loosening Monitoring Based on Modified Unet and Multitask Learning

et al. 2023

IEEE Sensors J.

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“…Researchers demonstrated that piezoelectric impedance measurements can be enhanced using circuitry elements such as inductive circuitries 4,11,20,28,29 and negative capacitance interfaces. [30][31][32] Both inductive and negative capacitance circuitries are advantageous for system dynamics optimization. The inductive circuitry provides additional resonant features in a small frequency range.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results showed that the FRF curves with inductance circuits are more sensitive to structural damage. Budoya et al 30 proposed a method that can improve the sensitivity of structural damage detection using a negative capacitance interface. In addition, Kim and Wang 4 enriched an EMI dataset by adjusting the inductance values in the proposed adaptive piezoelectric circuitry for mathematical-model-based damage identification.…”

Section: Introductionmentioning

confidence: 99%

1D-CNN-based damage identification method based on piezoelectric impedance using adjustable inductive shunt circuitry for data enrichment

Zhang

Wang

Hou

et al. 2022

Structural Health Monitoring

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The electromechanical impedance (EMI)-based damage identification method is a non-destructive testing approach in the field of structural health monitoring. The frequency response function (FRF) of EMI can effectively reveal the health conditions of a structure. Typically, the health condition is identified by comparing the FRF of a structure to that of a baseline. However, baselines may exhibit unpredictable shifts in real applications. In this study, a new EMI-based health identification method is proposed without reference to baselines or handcrafted features. An adjustable inductive shunt circuit that can enrich the EMI dataset is connected to a piezoelectric transducer. Pre-set damage, including bolt looseness and mass variations, are selected to demonstrate damage identification. The FRFs are extracted using a phase-sensitive detection algorithm. The damage identification model is realized using a one-dimensional convolutional neural network. Experimental results show that the proposed method can identify the location of bolt loosening and mass variation with an overall accuracy of 99.24%. The proposed method can be applied for identifying the health conditions of a structure with strong nonlinearity.

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Piezoelectric coupling as a feature sensitive to structural alterations

Berardengo,

Brambilla,

Codina

et al. 2025

Mechanical Systems and Signal Processing

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Sensitivity Enhancement of Piezoelectric Transducers for Impedance-Based Damage Detection via a Negative Capacitance Interface

Cited by 8 publications

References 21 publications

Electromechanical Impedance Temperature Compensation and Bolt Loosening Monitoring Based on Modified Unet and Multitask Learning

Electromechanical Impedance Temperature Compensation and Bolt Loosening Monitoring Based on Modified Unet and Multitask Learning

1D-CNN-based damage identification method based on piezoelectric impedance using adjustable inductive shunt circuitry for data enrichment

Piezoelectric coupling as a feature sensitive to structural alterations

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