Model-based structural health monitoring of naval ship hulls

Abstract The present paper reports on results from an ongoing research program at Cornell University aimed at employing model-based structural health monitoring techniques within new and existing naval hull structures. The techniques discussed involve the solution of inverse problems, formulated using both optimization-based and Bayesian approaches. The forward modeling capability is handled using a specially developed hull structural analysis tool, CU-BEN, while the solution of the inverse problem is handled using stochastic search methods that are part of a dedicated inverse solution algorithm “toolbox,” CU-PSST. Results from the application of these tools to problems of detecting section loss in hull plating due to corrosion, and isolating damaged framing due to an internal blast, are discussed.

[1]  Rune Brincker,et al.  Vibration Based Inspection of Civil Engineering Structures , 1993 .

[2]  R. L. Hardy.  Theory and applications of the multiquadric-biharmonic method : 20 years of discovery 1968-1988 , 1990 .

[3]  Javid Bayandor,et al.  Damage assessment and monitoring of composite ship joints , 2005 .

[4]  Klaus-Jürgen Bathe,et al.  A study of three‐node triangular plate bending elements , 1980 .

[5]  H. Saunders,et al.  Matrix Structural Analysis , 1979 .

[6]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[7]  Dave Higdon,et al.  A Bayesian approach to characterizing uncertainty in inverse problems using coarse and fine-scale information , 2002, IEEE Trans. Signal Process..

[8]  P. Moral.  Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications , 2004 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  A. S. J. Swamidas,et al.  Crack identification in a cross-stiffened plate system using the root mean square of time domain responses , 2006 .

[11]  David A. Nix,et al.  Vibration–based structural damage identification , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[12]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[13]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[14]  Achmad Zubaydi,et al.  Damage identification in a ship’s structure using neural networks , 2002 .

[15]  Gardar Johannesson,et al.  Stochastic Engine Final Report: Applying Markov Chain Monte Carlo Methods with Importance Sampling to Large-Scale Data-Driven Simulation , 2004 .

[16]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[17]  I. Weir.  Fully Bayesian Reconstructions from Single-Photon Emission Computed Tomography Data , 1997 .

[18]  B. Reddy,et al.  Introductory Functional Analysis: With Applications to Boundary Value Problems and Finite Elements , 1997 .

[19]  Henning Omre,et al.  Uncertainty in Production Forecasts based on Well Observations, Seismic Data and Production History , 2001 .

[20]  Rustem V. Shaikhutdinov,et al.  Bayesian State Estimation Method for Nonlinear Systems and Its Application to Recorded Seismic Response , 2006 .

[21]  Gui-rong Liu,et al.  Computational Inverse Techniques in Nondestructive Evaluation , 2003 .

[22]  James L. Beck,et al.  Reliability‐based robust control for uncertain dynamical systems using feedback of incomplete noisy response measurements , 2003 .

[23]  A. Ravindran,et al.  Engineering Optimization: Methods and Applications , 2006 .

[24]  Jean-Louis Batoz,et al.  An explicit formulation for an efficient triangular plate‐bending element , 1982 .

[25]  Michael Goldstein,et al.  Bayesian Forecasting for Complex Systems Using Computer Simulators , 2001 .

[26]  Phaedon-Stelios Koutsourelakis,et al.  A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters , 2008, J. Comput. Phys..

[27]  James O. Berger,et al.  A Bayesian analysis of the thermal challenge problem , 2008 .

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

[29]  Martin B. Hansen,et al.  Bayesian inversion of geoelectrical resistivity data , 2003 .

[30]  David R. Bickel.  Comment on "Sequential Monte Carlo for Bayesian Computation" (P. Del Moral, A. Doucet, A. Jasra) , 2006 .

[31]  K. Bathe.  Finite Element Procedures , 1995 .

[32]  Jonathan M. Nichols,et al.  A Bayesian approach to identifying and tracking damage in structures , 2009 .

[33]  Geir Sagvolden,et al.  Development and Applications of Full-Scale Ship Hull Health Monitoring Systems for the Royal Norwegian Navy , 2005 .

[34]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

[35]  K Lindemann,et al.  ON THE APPLICATION OF HULL SURVEILLANCE SYSTEMS FOR INCREASED SAFETY AND IMPROVED STRUCTURAL UTILIZATION IN ROUGH WEATHER , 1977 .

[36]  Sandeep T. Vohra,et al.  Ship hull structure monitoring using fibre optic sensors , 2001 .

[37]  D. M. Schmidt,et al.  Bayesian inference applied to the electromagnetic inverse problem , 1998, Human brain mapping.

[38]  Achmad Zubaydi,et al.  On the use of the autocorrelation function to identify the damage in the side shell of a ship's hull , 2000 .

[39]  Jun S. Liu,et al.  Sequential importance sampling for nonparametric Bayes models: The next generation , 1999 .

[40]  Klaus-Jürgen Bathe,et al.  A simple and effective element for analysis of general shell structures , 1981 .

[41]  Herbert K. H. Lee,et al.  Multiscale Modeling: A Bayesian Perspective , 2007 .

[42]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[43]  M A Crisfield,et al.  IVANOV'S YIELD CRITERION FOR THIN PLATES AND SHELLS USING FINITE ELEMENTS , 1979 .

[44]  Michael I Friswell,et al.  Damage identification using inverse methods , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[45]  Mark Seaver,et al.  Bragg grating-based fibre optic sensors in structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[46]  Mahmoud Haddara,et al.  Identification of Small-Sized Cracks on Cross-Stiffened Plate Structures for Ships , 2006 .

[47]  James L. Beck,et al.  Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning , 2008 .

[48]  Nicholas Zabaras,et al.  Hierarchical Bayesian models for inverse problems in heat conduction , 2005 .

[49]  P. Kitanidis.  Parameter Uncertainty in Estimation of Spatial Functions: Bayesian Analysis , 1986 .

[50]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[51]  Mike West,et al.  Markov Random Field Models for High-Dimensional Parameters in Simulations of Fluid Flow in Porous Media , 2002, Technometrics.

[52]  Israel Herszberg,et al.  Health monitoring of marine composite structural joints using fibre optic sensors , 2006 .

[53]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[54]  Christopher J. Earls,et al.  A posteriori initial imperfection identification in shell buckling problems , 2008 .

[55]  Charles R. Farrar,et al.  Structural health monitoring for ship structures , 2009 .

[56]  Hoon Sohn,et al.  A review of structural health monitoring literature 1996-2001 , 2002 .

[57]  Roberto Lopez-Anido,et al.  Structural health monitoring of marine composite structural joints using embedded fiber Bragg grating strain sensors , 2009 .