Bolt-Loosening Identification by Using Empirical Mode Decomposition and Sample Entropy

Lu, Guangtao; Wu, Longyun; Wang, Jiacheng; Yang, Dan; Wang, Tao

doi:10.1109/jsen.2023.3271607

Cited by 12 publications

(1 citation statement)

References 38 publications

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“…Consequently, detecting early-stage loosening near the rated bolt preload becomes challenging. To deal with the energy saturation, Lu et al [31] proposed an improved intrinsic empirical mode decomposition algorithm to extract nonlinear characteristics of intrinsic mode functions (IMFs) associated with the preload. The reconstructed IMFs were then utilized, and the normalized sample entropy of the new signal was used as an indicator to characterize the bolt preload.…”

Section: Introductionmentioning

confidence: 99%

Early bolt looseness monitoring using the leading waves energy in piezoelectric active sensing

Wang,

Yang

et al. 2024

Smart Mater. Struct.

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Bolt monitoring plays a vital role in ensuring the safe operation of engineering structures. The utilization of piezoelectric active sensing and analysis of ultrasonic energy transmitted through the interface of bolted connections has demonstrated high feasibility for monitoring bolt looseness. However, the ultrasonic energy saturation effect (i.e. the response signal energy changes slightly as the bolt preload variation) near the rated bolt preload restricts its applicability in early detection of bolt loosening. In this paper, for the energy saturation in the piezoelectric active sensing method, a new bolt looseness indicator with the energy of leading waves (i.e. the first several wave packets) in the response signal is proposed for bolt loosening monitoring, especially for the early bolt loosening monitoring since the energy of the leading wave packets has the linear relationship with bolt preload. The experimental results show thatthe energy of theindicator of bolt prfirst severaleload wave packets in the response signals can be as a. Within the entire range of bolt preload, the indicator loosenessexhibits a linear relationship with the bolt preload. Moreover, this method successfully resolves the challenge of energy saturation, providing an effective approach for monitoring bolt preload.

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Section: Introductionmentioning

confidence: 99%