A novel method for bonding state detection of explosive clad structure based on the Hilbert envelop energy ratio of mono-model signals. (11th October 2018)

• Record Type: Journal Article
• Authors: Si, Yue; Zhang, Zhousuo; Zhao, Bohan; Kong, Lingfei; Li, Shujuan
• Is Part Of: Smart materials and structures. Volume 27:Number 11(2018:Nov.)
• Publication Date: 2018-10-11
• Subjects: bonding state detection -- explosive clad structure -- Hilbert envelop energy ratio -- mono-model signal; Smart materials -- Periodicals; Strucural design -- Periodicals; 620.11
• DOI: 10.1088/1361-665X/aae124
• Languages: English
• ISSNs: 0964-1726

Abstract

Abstract: It is significant to perform bonding state detection of explosive clad structure to avoid serious accidents. Due to the complexity of its bonding interface, constructing the detection index which is sensitive to the change of the bonding state has turned into a challenging task. Therefore, a novel method based on the Hilbert envelop energy ratio of mono-model signals is proposed for this task. Benefiting from the advantage of only related with intrinsic property parameters such as damping ratios, natural frequencies and principal modes; the Hilbert envelope energy ratio of arbitrary two order model signals from impulse vibration response can reflect the inherent property of the multi-degree-of-freedom (MDOF) system. In addition, it has been found that the bonding state of explosive clad structure is mainly related with the structural damping ratio. Introduce the general characteristic of MDOF system into explosive clad structure, the detection index based on the Hilbert envelope energy ratio of mono-model signals is constructed for the bonding state detection of explosive clad structure. The proposed method is applied to the bonding state detection of explosive clad pipes to validate its robustness and effectiveness. Compared with the detection method based on optimized dual-tree complex wavelet transform (ODTCWT) and time entropy index proposed by the authors in previous research, the results indicate that the robustness and effectiveness of the proposed method in this paper is more superior to that of the detection method based on ODTCWT and time entropy index.