Prognostics Design for Structural Health Management

[1]  Shankar Sankararaman,et al.  Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction , 2015 .

[2]  K. Goebel,et al.  Bayesian model selection and parameter estimation for fatigue damage progression models in composites , 2015 .

[3]  Kai Goebel,et al.  Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods , 2014, IEEE Trans. Reliab..

[4]  K. Goebel,et al.  Analytical algorithms to quantify the uncertainty in remaining useful life prediction , 2013, 2013 IEEE Aerospace Conference.

[5]  Bhaskar Saha,et al.  Requirements Flowdown for Prognostics and Health Management , 2012, Infotech@Aerospace.

[6]  Aditi Chattopadhyay,et al.  Condition Based Structural Health Monitoring and Prognosis of Composite Structures under Uniaxial and Biaxial Loading , 2010 .

[7]  Sankalita Saha,et al.  Requirements Specification for Prognostics Performance - An Overview , 2010 .

[8]  Zhonghua Han,et al.  Efficient Uncertainty Quantification using Gradient-Enhanced Kriging , 2009 .

[9]  George Vachtsevanos,et al.  Methodologies for uncertainty management in prognostics , 2009, 2009 IEEE Aerospace conference.

[10]  Habib N. Najm,et al.  Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics , 2009 .

[11]  G. Kacprzynski,et al.  Advances in uncertainty representation and management for particle filtering applied to prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[12]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[13]  Jeong-Beom Ihn,et al.  A Potential Link from Damage Diagnostics to Health Prognostics of Composites through Built-in Sensors , 2007 .

[14]  Simon J. Godsill,et al.  An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.

[15]  Charles R Farrar,et al.  Damage prognosis: the future of structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Janis Varna,et al.  Constitutive Relationships for Laminates with Ply Cracks in In-plane Loading , 2005 .

[17]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[18]  James R. Van Zandt A more robust unscented transform , 2001 .

[19]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

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

[21]  Michael A. West,et al.  Combined Parameter and State Estimation in Simulation-Based Filtering , 2001, Sequential Monte Carlo Methods in Practice.

[22]  Stephen J. Engel,et al.  Prognostics, the real issues involved with predicting life remaining , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[23]  Roberts Joffe,et al.  Analytical modeling of stiffness reduction in symmetric and balanced laminates due to cracks in 90° layers , 1999 .

[24]  Hisashi Tanizaki,et al.  Nonlinear and non-Gaussian state-space modeling with Monte Carlo simulations , 1998 .

[25]  R. Caflisch Monte Carlo and quasi-Monte Carlo methods , 1998, Acta Numerica.

[26]  Wei-Liem Loh On Latin hypercube sampling , 1996 .

[27]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[28]  Y.-T. Wu,et al.  COMPUTATIONAL METHODS FOR EFFICIENT STRUCTURAL RELIABILITY AND RELIABILITY SENSITIVITY ANALYSIS , 1993 .

[29]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[30]  Peter Gudmundson,et al.  An analytic model for thermoelastic properties of composite laminates containing transverse matrix cracks , 1993 .

[31]  John A. Nairn,et al.  The initiation and growth of delaminations induced by matrix microcracks in laminated composites , 1992 .

[32]  C. Cornell,et al.  Adaptive Importance Sampling , 1990 .

[33]  Donald L. Iglehart,et al.  Importance sampling for stochastic simulations , 1989 .

[34]  Lars Tvedt,et al.  Second Order Reliability by an Exact Integral , 1989 .

[35]  M. Hohenbichler,et al.  Improvement Of Second‐Order Reliability Estimates by Importance Sampling , 1988 .

[36]  C. Bucher Adaptive sampling — an iterative fast Monte Carlo procedure , 1988 .

[37]  A. Kiureghian,et al.  Second-Order Reliability Approximations , 1987 .

[38]  Zvi Hashin,et al.  Analysis of cracked laminates: a variational approach , 1985 .

[39]  K. Doliński,et al.  First-order second-moment approximation in reliability of structural systems: Critical review and alternative approach , 1982 .

[40]  R. Rackwitz,et al.  First-order concepts in system reliability , 1982 .

[41]  J. E. Bailey,et al.  Multiple transverse fracture in 90° cross-ply laminates of a glass fibre-reinforced polyester , 1977 .

[42]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[43]  M. Rosenblatt Remarks on Some Nonparametric Estimates of a Density Function , 1956 .