Please use this identifier to cite or link to this item: https://doi.org/10.1177/1687814018808698
Title: Looseness localization for bolted joints using Bayesian operational modal analysis and modal strain energy
Authors: Hu, Y.-J.
Guo, W.-G.
Jiang, C.
Zhou, Y.-L. 
Zhu, W.
Keywords: Bayesian operational modal analysis
bolted joints
damage detection
Looseness localization
modal strain energy
Issue Date: 2018
Publisher: SAGE Publications Inc.
Citation: Hu, Y.-J., Guo, W.-G., Jiang, C., Zhou, Y.-L., Zhu, W. (2018). Looseness localization for bolted joints using Bayesian operational modal analysis and modal strain energy. Advances in Mechanical Engineering 10 (11). ScholarBank@NUS Repository. https://doi.org/10.1177/1687814018808698
Rights: Attribution 4.0 International
Abstract: Bayesian operational modal analysis and modal strain energy are employed for determining the damage and looseness of bolted joints in beam structures under ambient excitation. With this ambient modal identification technique, mode shapes of a damaged beam structure with loosened bolted connections are obtained based on Bayesian theory. Then, the corresponding modal strain energy can be calculated based on the mode shapes. The modal strain energy of the structure with loosened bolted connections is compared with the theoretical one without bolted joints to define a damage index. This approach uses vibration-based nondestructive testing of locations and looseness of bolted joints in beam structures with different boundary conditions by first obtaining modal parameters from ambient vibration data. The damage index is then used to identify locations and looseness of bolted joints in beam structures with single or multiple bolted joints. Furthermore, the comparison between damage indexes due to different looseness levels of bolted connections demonstrates a qualitatively proportional relationship. � The Author(s) 2018.
Source Title: Advances in Mechanical Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/214021
ISSN: 16878132
DOI: 10.1177/1687814018808698
Rights: Attribution 4.0 International
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