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

In bolted connection structures, some nonlinear effect takes place when the ultrasonic wave travels through the contact area of the parts, and this contact nonlinear characteristics is influenced by the preload of bolts. In this study, an improved empirical mode decomposition (IEMD) algorithm is dev...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2023-06, Vol.23 (12), p.1-1
Main Authors: Lu, Guangtao, Wu, Longyun, Wang, Jiacheng, Yang, Dan, Wang, Tao
Format: Article
Language:English
Subjects:
Algorithms
Bolt Connected Structures
Bolt Loosening Identification
Bolted joints
Empirical Mode Decomposition
Entropy
Loosening
Sample Entropy
Tightening
Tightening Torque
Torque
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In bolted connection structures, some nonlinear effect takes place when the ultrasonic wave travels through the contact area of the parts, and this contact nonlinear characteristics is influenced by the preload of bolts. In this study, an improved empirical mode decomposition (IEMD) algorithm is developed to extract the intrinsic mode functions (IMFs) related to the preload-dependent nonlinear characteristics, and then these ones are reconstructed. At last, the normalized sample entropy of the new signal is employed for bolt preload and loosening state detection. To validate this new method, an experimental setup with four specimens is designed and fabricated. The experimental results of three specimens show that the normalized entropy decreases monotonously from 1.35 to 1 as the tightening torque increases from 5Nm to 20Nm. Moreover, the Pearson's coefficient r of the fitted lines is greater than 0.99, and the relative errors of the slope and intercept of the fitted lines are no more than 3.0%. After this linear relationship is obtained, the tightening torque of the fourth specimen is estimated, and the relative error between the estimated and actual torque of specimen is 0.8% ~ 7%, which demonstrates that this proposed method can detect the tightening torque accurately. In addition, compared to the traditional energy-based index, the saturation phenomenon is not observed in the entropy when the torque is near to the rated one, which indicates that the new approach can identify the early loosening state of bolts and its identification performance is much more efficient.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3271607