Browsing by Author "Wang, Furui"
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Item INTELLIGENT DETECTION OF BOLT LOOSENESS USING STRUCTURAL HEALTH MONITORING METHODS AND PERCUSSION APPROACH(2020-12) Wang, Furui; Song, Gangbing; Franchek, Matthew A.; Grigoriadis, Karolos M.; Chen, Zheng; Mo, Yi-LungBolted 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.