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An investigation on bolt stress ultrasonic measurement based on acoustic time difference algorithm with adaptive hybrid extended Kalman filter.

Authors :
Quan, Xusong
Lv, Hongru
Liu, Changchun
Wang, Hui
Wu, Dongbo
Chen, Ping
Zhou, Hai
Source :
Measurement (02632241). Dec2021, Vol. 186, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

• Acoustic time difference algorithm can reduce error of acoustic time difference. • Acoustic time difference algorithm can improve bolt stress detection accuracy. • Kalman nonlinear filter is constructed to select initial values and observe noise. • Filter and cross-correlation algorithm can estimate the acoustic time difference. Ultrasonic measurement is an effective technical method for the bolt stress detection of the precise structural connection. This paper proposed an acoustic time difference algorithm based on adaptive hybrid extended Kalman filter to reduce the calculation error of acoustic time difference in bolt stress ultrasonic measurement. The initial value is firstly selected by particle filter algorithm, and the noise variance is measured by the Sage-Husa estimator. Then, a Kalman nonlinear filter is constructed to adaptively select initial values and observe noise. Thirdly, the filter and cross-correlation algorithm are used to accurately estimate the acoustic time difference. Finally, the signal simulation method and the ultrasonic measurement platform of bolt axial stress are built, and the proposed algorithm and the traditional algorithm are respectively used to calibrate and measure the correspondence between acoustic time difference and axial stress of the M8 coarse tooth half-thread bolt. The bolt tightening experimental and simulation results showed that the processed signal algorithm is verified and can effectively improve the measurement accuracy of bolt axial stress. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02632241
Volume :
186
Database :
Academic Search Index
Journal :
Measurement (02632241)
Publication Type :
Academic Journal
Accession number :
153413644
Full Text :
https://doi.org/10.1016/j.measurement.2021.110223