A novel damage index integrating piezoelectric impedance and ultrasonic guided wave for damage monitoring of bolted joints

With the ongoing evolution of microelectronic devices toward lower power consumption, the utilization of piezoelectric materials for energy harvesting from wind-induced vibrations has garnered considerable attention. This study employs a combined approach involving finite element analysis (FEA) and experiments to investigate the energy harvesting efficiency of the multi-stable piezoelectric wind energy harvester (MPWEH) and compares its performance with two alternative systems. The MPWEH demonstrates higher strains in both the x and y directions during reciprocating cross-well vibrations, establishing its superior energy harvesting efficiency compared to the alternative systems. Notably, at a wind speed of 8 m/s, the MPWEH generates an output power nearly six times higher than Local Bistable Piezoelectric Energy Harvester (LBPEH). The MPWEH achieves the maximum power density of 9.8125 mW/cm³, whereas the LBPEH registers the power density of 1.625 mW/cm³. The experimental results indicate that, under the optimal load resistance of 40 kΩ and a wind speed of 14 m/s, the MPWEH achieves a peak output power of 2.76 mW, with a power density of 17.25 mW/cm³. The versatile applicability of the MPWEH extends across various low-power consumption microelectronic devices, positioning it as a valuable candidate for empowering continuous monitoring sensors in diverse domains.

Section: Introductionmentioning

confidence: 99%

Harnessing multi-stable piezoelectric systems for enhanced wind energy harvesting

Liu,

Tao,

Jia

et al. 2024

“…In the past decades, various methods have been used to bolt looseness identification, such as vibration-based methods [5,6], electro-mechanical impedance methods [7,8], and ultrasonic-based methods [9,10]. Among these methods, vibration-based methods perform better in detecting overall structural damage [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Among these methods, vibration-based methods perform better in detecting overall structural damage [5,6]. The piezoelectric impedance-based method only requires one transducer and is easy to implement, however the method is sensitive and prone to the environment influence [7,8]. The conventional ultrasound-based methods, such as active sensing methods, usually use energy as an indicator and suffer from the saturation phenomenon [9,10].…”

Section: Introductionmentioning

confidence: 99%

An efficient robotic-assisted bolt-ball joint looseness monitoring approach using CBAM-enhanced lightweight ResNet

Li,

Yuan,

et al. 2023

Bolt-ball joints are widely used in space structures, and their looseness may lead to major safety accidents. The current bolt monitoring methods based on deep learning usually have high computational complexity, and it is difficult to guarantee its computational efficiency under practical scenario. To mitigate this problem, here in this paper, an efficient robotic-assisted bolt-ball joint looseness monitoring approach using convolutional block attention module (CBAM)-enhanced Lightweight ResNet is proposed. Firstly, the robotic-assisted tapping method is applied to bolt-ball joints to generate audio signals, which are constructed into time-frequency maps by continuous wavelet transform (CWT). Secondly, the original ResNet is improved as a lightweight network, which successfully reduces model complexity, and employs time-frequency maps as input. Then, CBAM is introduced to capture global information and focus on the critical feature. Thus, the efficiency of feature extraction is significantly improved. Finally, by the overall optimized structure, a CBAM-enhanced Lightweight ResNet model is established to monitor the bolt-ball joints looseness state accurately. Experimental results demonstrate the high efficiency while maintaining very lightweight structure of the proposed approach, verifying the effectiveness and superiority of the robot-assisted approach using CBAM-enhanced lightweight ResNet over other methods.

“…Currently, the sensors commonly used in SHM mainly include fiber optic sensing, piezoelectric sensing, stress-strain sensing, eddy current sensing, and computer vision technology [2]. Among them, piezoelectric sensors based on the piezoelectric effect have become one of the key SHM technologies due to their high sensitivity, simple control system, light weight, low cost, low energy consumption, and duality of sensing and driving [3,4]. Thanks to the superior performance of piezoelectric sensors, the impact load identification technology built based on them shows the advantages of simple operation, fast and efficient, wide detection range, and high applicability [5].…”

Section: Introductionmentioning

confidence: 99%

A hybrid FCN-BiGRU with transfer learning for low-velocity impact identification on aircraft structure

Huang

Liao

Sun

et al. 2023

Self Cite

In the work process of an aircraft, its external structure frequently encounters some low-velocity impact events, resulting in some barely visible impact damages inside the structure. Therefore, impact localization and reconstruction are critical for the structure's health monitoring and reliability analysis. However, traditional inversion methods usually perform poorly in identifying random impact loads due to a series of problems. Inspired by the superior model properties of deep learning algorithms, a novel feature learning-based technique for impact load reconstruction and localization is presented. The proposed method consists of two parts, the first of which is utilized to localize the impact loads and is referred to as One-dimensional Full Convolution Network (1D-FCN). The other part aims at reconstructing the impact load and is called Attention mechanism-Full Convolutional Network-Bidirectional Gating Recurrent Unit-Multilayer Perceptron (A-FCN-BiGRU-MLP). Moreover, a transfer learning technique is introduced to optimize the model structure and process in light of the suggested innovative network model. The experimental piece of this research was an aircraft cutting segment whose structural surface was equipped with a number of piezoelectric sensors. These sensors received impact response signals with differences in amplitude from hammer strikes at various positions. In the work, the process of locating first and then reconstructing the impact load history was followed. And the influence of implementing the transfer learning mechanism on the performance of the impact load history reconstruction model was investigated as well. Three experiments and repeated cross-validation verified the efficacy of the proposed method. The findings indicated that the proposed technique could accurately and quickly detect impact loads at various locations. Simultaneously, it also allowed a perfect reconstruction of the impact loads of different impact energies.