Smart Structures and Systems

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Bolt looseness detection and localization using time reversal signal and neural network techniques

  • Duan, Yuanfeng (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Sui, Xiaodong (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Tang, Zhifeng (Institute of Advanced Digital Technologies and Instrumentation, Zhejiang University) ;
  • Yun, Chungbang (College of Civil Engineering and Architecture, Zhejiang University)
  • Received : 2022.03.10
  • Accepted : 2022.07.21
  • Published : 2022.10.25
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Abstract

It is essential to monitor the working conditions of bolt-connected joints, which are widely used in various kinds of steel structures. The looseness of bolts may directly affect the stability and safety of the entire structure. In this study, a guided wave-based method for bolt looseness detection and localization is presented for a joint structure with multiple bolts. SH waves generated and received by a small number (two pairs) of magnetostrictive transducers were used. The bolt looseness index was proposed based on the changes in the reconstructed responses excited by the time reversal signals of the measured unit impulse responses. The damage locations and local damage severities were estimated using the damage indices from several wave propagation paths. The back propagation neural network (BPNN) technique was employed to identify the local damages. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the total damage severity can be successfully detected under the effect of external force and measurement noise. The local damage severity can be estimated reasonably for the experimental data using the BPNN constructed by the training patterns generated from the finite element simulations.

Keywords

Acknowledgement

The authors acknowledge the supports from the National Key R&D Program of China (2018YFE0125400, 2019YFE0112600) and National Natural Science Foundation of China (Grant Nos. 52078459, U1709216).

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