Some Recent Developments in Structural Health Monitoring

This paper is concerned with reporting some recent developments in Structural Health Monitoring (SHM) research conducted within the Dynamics Research Group at the University of Sheffield. The particular developments discussed are concerned with arguably the two main problems facing data-based approaches to SHM, namely: how to obtain data from damage states of a structure for supervised learning and how to remove environmental and operational effects from data when unsupervised learning (novelty detection) is indicated.

[1]  Hoon Sohn,et al.  Effects of environmental and operational variability on structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  Keith Worden,et al.  The use of pseudo-faults for novelty detection in SHM , 2010 .

[3]  T. Cipra.  Statistical Analysis of Time Series , 2010 .

[4]  Keith Worden,et al.  Damage detection in an aircraft component model , 2001 .

[5]  Roger Perman,et al.  Cointegration: An Introduction to the Literature , 1991 .

[6]  Alex M. Andrew.  An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50) , 2000, Robotica.

[7]  W. Fuller,et al.  LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOT , 1981 .

[8]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[9]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Charles R. Farrar,et al.  The fundamental axioms of structural health monitoring , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[12]  Keith Worden,et al.  DAMAGE DETECTION USING OUTLIER ANALYSIS , 2000 .

[13]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[14]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[15]  Keith Worden,et al.  Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data , 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[17]  Keith Worden,et al.  Damage identification using support vector machines , 2001 .

[18]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[19]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[20]  W. R. Buckland,et al.  Outliers in Statistical Data , 1979 .

[21]  Theodore Bartkowicz,et al.  An experimental study for damage detection using a hexagonal truss , 2001 .

[22]  Alexander J. Smola,et al.  Support vector machine learning , 2001, Tutorial Guide. ISCAS 2001. IEEE International Symposium on Circuits and Systems (Cat. No.01TH8573).

[23]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .