Steel and Composite Structures

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State detection of explosive welding structure by dual-tree complex wavelet transform based permutation entropy

  • Si, Yue (School of Mechanical Engineering, Xi'an Jiaotong University) ;
  • Zhang, ZhouSuo (School of Mechanical Engineering, Xi'an Jiaotong University) ;
  • Cheng, Wei (School of Mechanical Engineering, Xi'an Jiaotong University) ;
  • Yuan, FeiChen (School of Mechanical Engineering, Xi'an Jiaotong University)
  • Received : 2014.06.26
  • Accepted : 2015.01.31
  • Published : 2015.09.25
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Abstract

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

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