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所属单位：航空学院
发表刊物：STRUCTURAL CONTROL & HEALTH MONITORING
关键字：Bayesian regularization composite structures impact force reconstruction impact location identification unscented Kalman filter
摘要：In structural health monitoring of composite structures, one important task is to detect and identify the low-velocity impact events, which may cause invisible internal damages. This paper presents a novel approach for simultaneously identifying the impact location and reconstructing the impact force time history acting on a composite structure using dynamic measurements recorded by a sensor network. The proposed approach consists of two parts: (1) an inner loop to reconstruct the impact force time history and (2) an outer loop to search for the impact location. In the inner loop, a newly developed inverse analysis method with Bayesian inference regularization is employed to solve the ill-posed impact force reconstruction problem using a state-space model. In the outer loop, a nonlinear unscented Kalman filter (UKF) method is used to recursively estimate the impact location by minimizing the error between the measurements and the predicted responses. The newly proposed impact load identification approach is illustrated by numerical examples performed on a composite plate. Results have demonstrated the effectiveness and applicability of the proposed approach to impact load identification. Copyright (c) 2016 John Wiley & Sons, Ltd.
ISSN号：1545-2255
是否译文：否
发表时间：2017-05-01
合写作者：Sun, Hao,Buyukozturk, Oral
通讯作者：严刚