Coupling effects with vibration-based estimation of individual bolt tension in multi-bolt structures

Brøns, Marie; Plaugmann, Axel; Fidlin, Alexander; Thomsen, Jon Juel

doi:10.1016/j.jsv.2022.116873

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…23 Based on the time-domain and frequency-domain analysis of collected vibration signals, many dynamic characteristics have been discussed experimentally, for example, natural frequencies, 24,25 transmissibility functions, 26 mode shapes, 27 and their ramifications. 28,29 Furthermore, time-frequency analysis is investigated to explore the relationship between the changes of damage indices and that of bolt preloads. [30][31][32] Lately, driven by machine learning algorithms, effective classifiers could be trained by sensitive features, 33 or directly tuned by raw vibration signals using deep learning architectures.…”

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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