Stochastic iterative modal identification algorithm and application in wireless sensor networks

SUMMARY Computational capability of wireless sensor network (WSN) significantly facilitates application of dense sensory arrays, which is increasingly important in health monitoring of large-scale structural systems. As the wireless sensor technology improves, more complicated tasks can be assigned to sensing units, and the communication between sensing nodes and their base station can be minimized, utilizing in-network processing. This strategy should be used to address WSN challenges, such as limited communication bandwidth and prohibitive power consumption, associated with wireless communication and battery power. An iterative modal identification algorithm is proposed in this paper, which uses the on-board processors for estimation of system parameters through iteration cycles. The iterative algorithm was originally developed such that each individual sensor, having an initial estimate of the system parameters, its local measurement, and the excitation signal, updates the estimated model and passes it through the network until convergence. This study further improves the algorithm to eliminate its limitations in need for availability of excitation load and initial estimate of the system parameters. As a result, the algorithm is applicable for modal identification of structural systems under ambient loading without need for prior information about the system parameters. The development of the algorithm is presented in this paper and validated through implementation on a numerically simulated example and a laboratory experiment. Furthermore, its performance is evaluated using data from an ambient vibration test of the Golden Gate Bridge using a WSN. Results of these implementations verify the functionality of the algorithm in monitoring of real-life structural systems. Copyright © 2012 John Wiley & Sons, Ltd. Received 10 May 2012; Revised 27 August 2012; Accepted 31 August 2012

[1]  Gul Agha,et al.  Reliable multi-hop communication for structural health monitoring , 2010 .

[2]  Jerome P. Lynch,et al.  Automated Modal Parameter Estimation by Parallel Processing within Wireless Monitoring Systems , 2008 .

[3]  A. B. Dunwoody,et al.  A Mathematical Basis for the Random Decrement Vibration Signature Analysis Technique , 1982 .

[4]  Anne S. Kiremidjian,et al.  A modular, wireless damage monitoring system for structures , 1998 .

[5]  J. Juang,et al.  Effects of Noise on Modal Parameters Identified by the Eigensystem Realization Algorithm , 1986 .

[6]  Daniel J. Inman,et al.  A wireless active sensing system for impedance-based Structural Health Monitoring , 2005 .

[7]  Jerome P. Lynch,et al.  Real-Time Damage Prognosis of High-Performance Fiber Reinforced Cementitious Composite Structures , 2004 .

[8]  David E. Culler,et al.  Design and Implementation of Scalable Wireless Sensor Network for Structural Monitoring , 2008 .

[9]  T. Moon.  The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[10]  The Math Works, Inc..  The Math Works Inc , 1991, International Conference on Advances in System Simulation.

[11]  S. R. Ibrahim,et al.  Random Decrement and Regression Analysis of Traffic Responses of Bridges , 1996 .

[12]  Billie F. Spencer,et al.  Distributed computing strategy for structural health monitoring , 2006 .

[13]  Minh Q. Phan,et al.  Identification and control of mechanical systems , 2001 .

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

[15]  H. A. Cole,et al.  On-the-line analysis of random vibrations. , 1968 .

[16]  Billie F. Spencer,et al.  Decentralized structural health monitoring using smart sensors , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[17]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[18]  Jerome P. Lynch,et al.  The Design of a Wireless Sensing Unit for Structural Health Monitoring , 2001 .

[19]  Shamim N. Pakzad,et al.  Development and deployment of large scale wireless sensor network on a long-span bridge , 2010 .

[20]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[21]  Farshad Khorrami,et al.  New integrated piezoelectric-dielectric microstrip antenna for dual wireless actuation and sensing functions , 1998, Smart Structures.

[22]  Shamim N. Pakzad,et al.  An Iterative Modal Identification Algorithm for Structural Health Monitoring Using Wireless Sensor Networks , 2013 .

[23]  Randall J. Allemang,et al.  THE MODAL ASSURANCE CRITERION–TWENTY YEARS OF USE AND ABUSE , 2003 .

[24]  Sung-Han Sim,et al.  Decentralized Strategies for Monitoring Structures using Wireless Smart Sensor Networks , 2009 .

[25]  J. C. Asmussen,et al.  Modal Analysis Based on the Random Decrement Technique: application to civil engineering structures , 1997 .

[26]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[27]  Billie F. Spencer,et al.  Structural health monitoring sensor development for the Imote2 platform , 2008, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[28]  Sung-Han Sim,et al.  Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks , 2011 .

[29]  Shamim N. Pakzad,et al.  Modified Natural Excitation Technique for Stochastic Modal Identification , 2013 .

[30]  John A. Crowe,et al.  Wireless monitoring of highways , 1999, Smart Structures.

[31]  Minh Q. Phan,et al.  Identification and Control of Mechanical Systems: Frontmatter , 2001 .

[32]  Hoon Sohn,et al.  Damage diagnosis using time series analysis of vibration signals , 2001 .

[33]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[34]  Shamim N. Pakzad,et al.  Statistical Analysis of Vibration Modes of a Suspension Bridge Using Spatially Dense Wireless Sensor Network , 2009 .

[35]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[36]  Richard S. Pappa,et al.  A consistent-mode indicator for the eigensystem realization algorithm , 1992 .

[37]  Jr B. F. Spencer,et al.  Structural Health Monitoring Using Smart Sensors , 2007 .

[38]  Gaurav S. Sukhatme,et al.  Networked Sensing for Structural Health Monitoring , 2004 .

[39]  Shamim N. Pakzad,et al.  Distributed modal identification using restricted auto regressive models , 2011, Int. J. Syst. Sci..

[40]  R. Shanmugam.  Introduction to Time Series and Forecasting , 1997 .

[41]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[42]  Fabio Casciati,et al.  A power harvester for wireless sensing applications , 2007 .