Bolt looseness detection using SH guided wave and wave energy transmission

Zhang, Mengru; Tang, Zhifeng; Yun, Chung Bang; Sui, Xiaodong; Chen, Jian; Duan, Yuanfeng

doi:10.1088/1361-665x/ac1d90

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…[13][14][15][16][17] In the realm of structural health monitoring using piezoelectric transducers, two categories emerge: passive and active methods. 18 Active methods combine actuators and sensors, categorized into EMI (Electro-Mechanical Impedance) 19,20 or GW (Guided Waves) 21,22 for structural inspection. 23,24 Passive methods solely rely on piezoelectric materials' sensing properties to output electrical signals with vibration characteristics.…”

Section: Introductionmentioning

confidence: 99%

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Chiu,

Lo,

Shu

2024

Active and Passive Smart Structures and Integrated Systems XVIII

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This article presents an innovative method for monitoring rotating instruments using piezoelectric array sensors and multi-task CNN (convolutional neural network) learning. The setup involves connecting piezoelectric patches, enabling spatial condition monitoring with a single voltage signal. The sensor array simultaneously tracks four bearing conditions and three gear conditions. However, a single-task CNN faces challenges, especially when trying to distinguish weak gear faults while simultaneously considering different bearing conditions. To address this, the article employs multi-task learning, simplifying the classification task by utilizing shared convolutional layers and two distinct fully connected layers dedicated to gear and bearing health states. This approach is also adaptable for extending fault detection to additional locations. Experiment demonstrates that multi-task learning achieved a 90% accuracy in identifying missing tooth defects in gears, outperforming the 78% accuracy of single-task learning. It confirms multi-task learning's effectiveness in detecting weak faults during multi-location defect monitoring using piezoelectric arrays.

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Section: Introductionmentioning

confidence: 99%

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Chiu,

Lo,

Shu

2024

Active and Passive Smart Structures and Integrated Systems XVIII

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“…In PZT-based methods, plentiful detection mechanisms and feature indicators have been developed, for example, ultrasonic attenuation, 10,11 time reversal, 12 electromechanical impedance, 13,14 and vibrationacoustic modulation. [15][16][17] Although the PZT-based methods are competent to detect local looseness, the effect driven by temperature, geometry, and material characteristics still hampers industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Yang

Zhang

Chen

2023

Structural Health Monitoring

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Bolted joints are the most widely utilized connection types in industries, and therein looseness identification of bolted structures is of great significance to guarantee structural reliability. In this article, a comprehensive study of bolt looseness identification under random excitation is presented. To fulfill this task, this research focuses on three prominent difficulties, including nonstationary signal processing, subtle feature extraction, and robust state classification. First, a novel filter bank structure of quasi-analytic dual-tree complex wavelet packet transform is constructed to analyze the measured vibration response signals, for purpose of capturing subtle feature information. Then, multiple features are extracted from subband signals to capture the variations of dynamic characteristics, and sensitive features are selected by Laplacian score to construct the low-dimensional feature set. Subsequently, a novel classifier with better generalization performance, named large margin distribution machine, is optimized with the wavelet kernel function and the whale optimization algorithm, in order to handle the intrinsic uncertainty related to the looseness states of bolted structures. After feeding the low-dimensional feature set, the proposed classifier is trained to identify looseness states of bolted structures. Finally, experiments of a two-bolt lapped beam under random excitation are conducted to verify the effectiveness of the proposed method, and two typical loading conditions (paired-bolt looseness and single-bolt looseness) are considered. Besides, the superiority of the proposed method is demonstrated by comparing with other analogical methods. This research can provide a promising implement in practical applications of bolt looseness identification under random excitation.

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“…Many advanced non-destructive testing technologies, including the electro-mechanical impedance (EMI) method [5], fiber optical sensing technique [6], percussion method [7] and ultrasonic method [8], have been widely explored for bolt looseness detection. EMI has demonstrated high sensitivity and effectiveness in detecting bolt loosening, but the sensing range is limited around piezoelectric (PZT) transducer and the results may be easily distorted by ambient interference [9]. Fiber optical sensors [10,11] have demonstrated effectiveness in bolt looseness detection while the installation and protection of fiber optical sensors is the critical obstacle for wider application in civil structures with harsh service environments.…”

Section: Introductionmentioning

confidence: 99%

“…The latter method, which utilizes the connected plates as the waveguide, has attracted increasing attention due to its wider sensing range. Any variation in the tightness state of bolt joints will alter the contact area between the connected assemblies and consequently disturb the guided wave propagation [9]. Yang and Chang [20,21] employed micro-contact theory to establish the relationship between the actual contact area and the leaked wave energy, and conducted experiments to validate the theoretical model.…”

Section: Introductionmentioning

confidence: 99%

Bolt tightness monitoring using multiple reconstructed narrowband Lamb waves combined with piezoelectric ultrasonic transducer

Li,

Liu,

Sun

et al. 2023

Smart Mater. Struct.

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Narrowband tone burst excitation with a carefully selected frequency is generally utilized to generate Lamb waves for mode purity, dispersion diminution and signal interpretability. Narrowband excitation exhibits saturation phenomenon and even non-monotonic trends with the development of bolt looseness. In this research, a piezoelectric ultrasonic transducer with high signal-to-noise ratio is designed and fabricated for bolt tightness monitoring. The chirp signal is utilized to drive the actuator to generate broadband Lamb waves in the connected plate, and a transfer function-based signal reconstruction algorithm is innovatively proposed to extract narrowband tone burst responses with different center frequencies from the broadband chirp response for bolt tightness monitoring. The leaked wave energy-based bolt tightness indexes (TIs) are calculated based on the extracted multiple tone burst responses and a feature-level data fusion strategy is proposed to combine the TIs for exploiting the merits of different-frequency inspecting waves for different bolt tightness conditions. The fused TI not only presents a monotonic tendency with the increasing of bolt tightness, but also tackles the low sensitivity of narrowband Lamb waves for embryo bolt looseness. The proposed method contributes a novel and stable scheme for bolt tightness monitoring and opens a new perspective for damage evaluation of structures.

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Bolt looseness detection using SH guided wave and wave energy transmission

Cited by 14 publications

References 38 publications

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Multi-location fault detection with piezoelectric arrays and multi-task CNN learning

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Bolt tightness monitoring using multiple reconstructed narrowband Lamb waves combined with piezoelectric ultrasonic transducer

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