Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

Summary In this paper, a Bayesian sequential sensor placement algorithm, based on the robust information entropy, is proposed for multi-type of sensors. The presented methodology has two salient features. It is a holistic approach such that the overall performance of various types of sensors at different locations is assessed. Therefore, it provides a rational and effective strategy to design the sensor configuration, which optimizes the use of various available resources. This sequential algorithm is very efficient due to its Bayesian nature, in which prior distribution can be incorporated. Therefore, it avoids the possible unidentifiability problem encountered in a sequential process, which starts with small number of sensors. The proposed algorithm is demonstrated using a shear building and a lattice tower with consideration of up to four types of sensors. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Mustafa Gul,et al.  Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering , 2011 .

[2]  Lambros S. Katafygiotis,et al.  Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study , 2004 .

[3]  Yi-Qing Ni,et al.  Information entropy based algorithm of sensor placement optimization for structural damage detection , 2012 .

[4]  Ernesto Heredia-Zavoni,et al.  Optimal instrumentation of uncertain structural systems subject to earthquake ground motions , 1998 .

[5]  Lambros S. Katafygiotis,et al.  An efficient simulation method for reliability analysis of linear dynamical systems using simple additive rules of probability , 2005 .

[6]  Sheng Zhan,et al.  Multi-Type Sensor Placement for Multi-Scale Response Reconstruction , 2013 .

[7]  J. Beck,et al.  Entropy-Based Optimal Sensor Location for Structural Model Updating , 2000 .

[8]  Lambros S. Katafygiotis,et al.  Model updating using noisy response measurements without knowledge of the input spectrum , 2005 .

[9]  James L. Beck,et al.  Structural protection using MR dampers with clipped robust reliability-based control , 2007 .

[10]  Mustafa Gul,et al.  Ambient Vibration Data Analysis for Structural Identification and Global Condition Assessment , 2008 .

[11]  A. Rama Mohan Rao,et al.  Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique , 2007 .

[12]  Ka-Veng Yuen.  Updating large models for mechanical systems using incomplete modal measurement , 2012 .

[13]  A. M. Abbas,et al.  Investigations into critical earthquake load models within deterministic and probabilistic frameworks , 2002 .

[14]  Costas Papadimitriou,et al.  Optimal Sensor Placement Methodology for Identification with Unmeasured Excitation , 2001 .

[15]  Ting-Hua Yi,et al.  Optimal sensor placement for structural health monitoring based on multiple optimization strategies , 2011 .

[16]  J. Beck,et al.  Updating Models and Their Uncertainties. I: Bayesian Statistical Framework , 1998 .

[17]  L. Michael Santi,et al.  Optimal Sensor Selection for Health Monitoring Systems , 2013 .

[18]  Costas Papadimitriou,et al.  Optimal experimental design in stochastic structural dynamics , 2005 .

[19]  F. Udwadia.  Methodology for Optimum Sensor Locations for Parameter Identification in Dynamic Systems , 1994 .

[20]  Costas Papadimitriou,et al.  Optimal sensor placement methodology for parametric identification of structural systems , 2004 .

[21]  Hong-Nan Li,et al.  Methodology Developments in Sensor Placement for Health Monitoring of Civil Infrastructures , 2012, Int. J. Distributed Sens. Networks.

[22]  Michele Meo,et al.  On the optimal sensor placement techniques for a bridge structure , 2005 .

[23]  L. L. Zhang,et al.  Optimal placement of sensors for structural health monitoring using improved genetic algorithms , 2004 .

[24]  J. Beck,et al.  Spectral density estimation of stochastic vector processes , 2002 .

[25]  Ka-Veng Yuen,et al.  Structural Health Monitoring of a Reinforced Concrete Building during the Severe Typhoon Vicente in 2012 , 2013, TheScientificWorldJournal.

[26]  Ka-Veng Yuen,et al.  Structural health monitoring of Canton Tower using Bayesian framework , 2012 .

[27]  J. Beck,et al.  Asymptotic Expansions for Reliability and Moments of Uncertain Systems , 1997 .

[28]  Lambros S. Katafygiotis,et al.  Bayesian spectral density approach for modal updating using ambient data , 2001 .

[29]  Ka-Veng Yuen,et al.  Ambient interference in long-term monitoring of buildings , 2010 .

[30]  Ka-Veng Yuen,et al.  Bayesian Methods for Updating Dynamic Models , 2011 .

[31]  Yi-Qing Ni,et al.  Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower , 2009 .

[32]  Hisham Abou-Kandil,et al.  SENSORS LOCATION FOR UPDATING PROBLEMS , 1999 .

[33]  Maria Pina Limongelli,et al.  Optimal location of sensors for reconstruction of seismic responses through spline function interpolation , 2003 .

[34]  Dongsheng Li,et al.  The connection between effective independence and modal kinetic energy methods for sensor placement , 2007 .

[35]  James L. Beck,et al.  Updating Properties of Nonlinear Dynamical Systems with Uncertain Input , 2003 .