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Bolted joints have been widely used to connect different components across multiple engineering fields, while the bolt looseness detection is an urgent issue to be solved. Recently, several piezo-enabled structural health monitoring (SHM) methods have been utilized to detect bolt looseness, including the active sensing method, electromechanical impedance (EMI) method, and the vibro-acoustic modulation (VAM) method. However, current approaches mostly focus on single-bolt looseness detection, and there is still a lack theoretical investigation to explore the principle of these methods. In this dissertation, several in-depth studies to advance the development of research in the bolt looseness detection are presented. First, a numerical model, a semi-analytical model, and an analytical model of the active sensing method for single-bolt looseness detection is proposed. Then, several new entropy-based indices are developed to replace the current index, i.e., signal energy. Via these entropy-based indices and machine learning (ML) technique, the detection of multi-bolt looseness is achieved for the first time. Second, a model to describe the relationship between bolt preload and EMI signal is theoretically developed, providing a better understanding of the EMI method. Third, in terms of the VAM method, swept sine waves as inputs are employed to improve practicability, and a new entropy-based index is developed to enable the VAM method to detect multi-bolt looseness. Moreover, considering that the above methods depend on permanent contact between transducers and structures, a new percussion-based approach is proposed. By tapping the bolted joint and analyzing the percussion-induced sound signals, the bolt looseness can be detected without contact-type sensors. First, an analytical model to research the mechanism of the percussion-based approach for bolt looseness detection is proposed. Then, by using deep learning (DL) based techniques to process and classify the percussion-induced sound signals under different bolt preloads, two practical percussion-based approaches to detect bolt looseness detection were developed. In summary, several in-depth investigations of SHM methods and a new percussion-based approach for bolt looseness detection have been conducted in this dissertation. It is believed that these methods have great potential for future industrial applications.



Bolted Joint, Structural Health Monitoring


Portions of this document appear in: Wang, Furui, Linsheng Huo, and Gangbing Song. "A piezoelectric active sensing method for quantitative monitoring of bolt loosening using energy dissipation caused by tangential damping based on the fractal contact theory." Smart Materials and Structures 27, no. 1 (2017): 015023.; Wang, Furui, Siu Chun Michael Ho, Linsheng Huo, and Gangbing Song. "A novel fractal contact-electromechanical impedance model for quantitative monitoring of bolted joint looseness." Ieee Access 6 (2018): 40212-40220.; Huo, Linsheng, Furui Wang, Hongnan Li, and Gangbing Song. "A fractal contact theory based model for bolted connection looseness monitoring using piezoceramic transducers." Smart Materials and Structures 26, no. 10 (2017): 104010.; Wang, Furui, and Gangbing Song. "Bolt early looseness monitoring using modified vibro-acoustic modulation by time-reversal." Mechanical Systems and Signal Processing 130 (2019): 349-360.; Wang, Furui, Siu Chun Michael Ho, and Gangbing Song. "Modeling and analysis of an impact-acoustic method for bolt looseness identification." Mechanical Systems and Signal Processing 133 (2019): 106249.; Wang, Furui, Zheng Chen, and Gangbing Song. "Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine." Mechanical Systems and Signal Processing 136 (2020): 106507.; Wang, Furui, Siu Chun Michael Ho, and Gangbing Song. "Monitoring of early looseness of multi-bolt connection: a new entropy-based active sensing method without saturation." Smart Materials and Structures 28, no. 10 (2019): 10LT01.; Wang, Furui, and Gangbing Song. "Monitoring of multi-bolt connection looseness using a novel vibro-acoustic method." Nonlinear Dynamics (2020): 1-12.; Wang, Furui, Gangbing Song, and Yi‐Lung Mo. "Shear loading detection of through bolts in bridge structures using a percussion‐based one‐dimensional memory‐augmented convolutional neural network." Computer‐Aided Civil and Infrastructure Engineering 36, no. 3 (2021): 289-301.; Wang, Furui, Aryan Mobiny, Hien Van Nguyen, and Gangbing Song. "If structure can exclaim: a novel robotic-assisted percussion method for spatial bolt-ball joint looseness detection." Structural Health Monitoring 20, no. 4 (2021): 1597-1608.; Wang, Furui, et al. “Smart Crawfish: A Concept of Underwater Multi-Bolt Looseness Identification Using Entropy-Enhanced Active Sensing and Ensemble Learning.” Mechanical Systems and Signal Processing, vol. 149, 2021, p. 107186., doi:10.1016/j.ymssp.2020.107186.