Bayesian operational modal analysis using auto-regressive model

摘要：

　　This paper proposes an innovative method for Bayesian operational modal analysis(OMA)based on auto-regressive(AR)model.The mathematical model of a dynamic system is constructed by an AR model.Following the Bayes theorem,the posterior PDF of the uncertain parameters of the AR model is derived.Identifying the AR model is thus viewing as a Bayesian inference problem where the posterior PDF is to be identified.Despite the fact that the AR model of a real structure has a large number of uncertain parameters,exploiting the linear property of a AR model,the most probable values and posterior uncertainties of the AR model parameters are analytically derived,facilitating an efficient application in practice.The modal parameters of a structure can be extracted from the parameter matrices of the AR model because it can be shown that an AR model describes a vibrating structure.Novel analytical formulations are proposed to propagate the uncertainties of the AR model parameters to the modal parameters.The proposed method is verified on a full-scale structure.Working directly on the measured accelerations,the proposed method can make use of the original information to quickly identify all modal parameters of interest with corresponding uncertainties in just a few minutes.

关键词： Bayesian operational modal analysis auto-regressive model uncertainty field test

作者: J.H.Yang H.F.Lam

作者单位: Research Institute of Structural Engineering and Disaster Reduction,College of Civil Engineering,Ton Department of Architecture and Civil Engineering,City University of Hong Kong,Hong Kong,China

会议类型: 国际会议

会议名称: The 7th World Conference on Structural Control and Monitoring(7WCSCM)(第七届结构控制与监测世界大会)

会议地点: 青岛

会议语种:英文

页码: 1806-1811

在线出版日期: 2018-07-22（万方平台首次上网日期，不代表论文的发表时间）