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 7 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%

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

Wang,

Yang

et al. 2024

Smart Mater. Struct.

Self Cite

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Bolt looseness monitoring using dynamic mode decomposition with piezoelectric active sensing

Tan,

Wang,

Fang

et al. 2024

Measurement

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Intelligent prediction of rock bolt debonding employing the fractal theory and relevance vector machine (FT-RVM) with piezoceramic transducers

Liu,

Bai,

et al. 2024

Smart Mater. Struct.

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A new intelligent prediction model incorporated fractal theory and relevance vector machine (FT-RVM) was proposed to detect the debonding status of the rock bolt by using the piezoceramic transducer-induced stress waves. In the FT-RVM model, the original signals under different debonding status are firstly decomposed by the wavelet packet decomposition, the box dimension of decomposed signal is extracted by fractal theory (FT). The fractal box dimension of decomposed signals and root mean square value of the original signal are used as the as the inputs to the FT-RVM model, and the different debonding status of the glass fiber reinforced polymer rock bolt is the output. After the training, the prediction model is used to estimate the debonding status of the rock bolt. In the FT-RVM model, the kernel function utilized is the Gaussian radial basis function, and its optimal value is obtained by using particle swarm optimization (PSO). The experimental results show that the average relative error of the FT-RVM prediction model is 3.04%, and the accuracy and reliability of the model are high, which demonstrates the intelligent identification of GFRP rock bolt debonding status. The proposed intelligent prediction model based on FT-RVM could be used to quickly evaluate rock bolt debonding status.

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

Cited by 7 publications

References 38 publications

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

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

Bolt looseness monitoring using dynamic mode decomposition with piezoelectric active sensing

Intelligent prediction of rock bolt debonding employing the fractal theory and relevance vector machine (FT-RVM) with piezoceramic transducers

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