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Journal ArticleDOI

Coda wave interferometry-based very early stage bolt looseness monitoring using a single piezoceramic transducer

Linsheng Huo
- 14 Feb 2022 - 
- Vol. 31, Iss: 3, pp 035030-035030
TLDR
In this article , a coda wave interferometry (CWI)-based high-resolution bolt preload monitoring using a single piezoceramic transducer is proposed.
Abstract
Abstract The traditional monitoring methods can only give warnings for the bolts with severe looseness. However, it is essential for the safety of bolted joints to detect the looseness of bolts at the very early stage. To this end, in this paper, coda wave interferometry (CWI)-based high-resolution bolt preload monitoring using a single piezoceramic transducer is proposed. According to the CWI and acoustoelastic theories, a theoretical model is established and the linear relationship between the time shifts of coda waves and the preload variations of the bolt is derived. An experiment, in which a piezoceramic transducer simultaneously functions as the actuator and sensor, was carried out to verify the effectiveness of the proposed method. Three lead zirconium titanate transducers at different locations of a bolted specimen are tested. The experimental results show that the time shifts of coda waves increase linearly with the decrease of bolt preload and the detectable resolution of bolt preload (DRBP) is up to 0.326%. The DRBP value proves that the proposed technique can successfully monitor bolt looseness at the very early stage. In addition, a comparison study is carried out between the CWI-based method and the energy-based wavelet packet decomposition (WPD) method, and the result shows that the preload sensitivity of the CWI-based method is about six times higher than that of the WPD approach. Therefore, the CWI-based method is an effective way for the in situ monitoring of bolt looseness, especially in the embryonic stage.

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Citations
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Percussion-based quasi real-time void detection for concrete-filled steel tubular structures using dense learned features

TL;DR: In this article , a percussion-based void detection method using dense learned features was proposed, which leverages CNN, GAP, and CAM to obtain high accuracy for detecting subsurface voids of concrete-filled steel tubular structures.
Journal ArticleDOI

Full-range bolt preload monitoring with multi-resolution using the time shifts of the direct wave and coda waves

TL;DR: In this paper , the authors proposed a full-range monitoring approach with multi-resolution using the time shifts of the direct wave and coda waves, which can be used as a low-resolution "probe" to monitor significant preload deterioration.
Journal ArticleDOI

Experimental Evaluation of Wearable Piezo Ring for Bolted Connection Monitoring Using the Active Sensing Approach

- 01 Mar 2023 - 
TL;DR: Wang et al. as mentioned in this paper designed a wearable piezoelectric ring based on a material is designed and it can be easily integrated into the bolt connection as a washer, which can be used to monitor the bolted connection to ensure the safety of the whole structure.
Journal ArticleDOI

Proof-of-concept study of bolt connection status monitoring using fiber Bragg grating curvature sensor

TL;DR: In this article , a novel method with fiber Bragg grating (FBG)-based curvature sensor is proposed for connection status monitoring of bolted joint groups, which can provide a general solution for the on-line monitoring of bolt connection status for large-span distributed bolted-joint groups.
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TL;DR: In this article, a more comprehensive review of recent literature in the broader area of mechanics of mechanically fastened joints in polymer-matrix composite structures is presented, including joint design methodologies, considerations of the influence of geometric effects and fastener preload selection.
Journal ArticleDOI

Vision-based detection of loosened bolts using the Hough transform and support vector machines

TL;DR: In this article, the horizontal and vertical lengths of the bolt head were calculated automatically using the Hough transform and other image processing techniques, and a linear support vector machine was trained with the aforementioned features, thereby building a robust classifier capable of automatically differentiating tight bolts from loose bolts.
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