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Degree:Doctoral Degree in Engineering
School/Department:College of Aerospace Engineering

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Education Level:南京航空航天大学

Alma Mater:南京航空航天大学

Paper Publications

A non-negative Bayesian learning method for impact force reconstruction
Date of Publication:2019-01-01 Hits:

Affiliation of Author(s):航空学院
Journal:J Sound Vib
Abstract:Detecting and identifying impact events, which may cause severe damages, is important in assessment of the integrity of many engineering structures. This paper presents a new approach for reconstructing the impact forces applied on engineering structures (e.g., composites) using sensor recordings of the structural responses. The problem is firstly formulated as a discretized deconvolution problem in the time domain with the impact force vector as the unknown. Then with consideration of the physical property of the impact force, an inverse analysis approach of Bayesian learning with non-negative regularization constraints is employed to solve the ill-posed deconvolution problem. The newly proposed impact force reconstruction approach is illustrated by experimental examples performed on a sandwich composite structure. Results have demonstrated the effectiveness and applicability of the proposed approach to reconstruct impact forces. © 2019 Elsevier Ltd
ISSN No.:0022-460X
Translation or Not:no
Date of Publication:2019-01-01
Co-author:Sun, Hao
Correspondence Author:yg
Date of Publication:2019-01-01