An energy-based prognostic framework to predict evolution of damage in composite materials

This chapter describes damage prognosis techniques in the context of structural health monitoring for aerospace materials, and illustrates the efficacy of the proposed methods using fatigue data from a graphite–epoxy composite coupon. Prognostics is a core element in health management sciences which aims to predict remaining useful lifetime of the systems or components through estimation of their future health state based on partial knowledge about the current health state and future system usage. The methods shown in this chapter use a physics-based modeling approach whereby the time-dependent behavior of the damaged material is idealized via mathematical equations that rely on physical principles. Rigorous mathematical tools are used to estimate the uncertainty associated with the prediction process. Information stemming from these predictions is usable in an operational context for informed decisions about safety and maintenance, among others.

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

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

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

[4]  Dawn An,et al.  Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab , 2013, Reliab. Eng. Syst. Saf..

[5]  John A. Nairn,et al.  The Strain Energy Release Rate of Composite Microcracking: A Variational Approach , 1989 .

[6]  Kaisa Simola,et al.  Application of stochastic filtering for lifetime prediction , 2006, Reliab. Eng. Syst. Saf..

[7]  Geir Storvik,et al.  Particle filters for state-space models with the presence of unknown static parameters , 2002, IEEE Trans. Signal Process..

[8]  Ramesh Talreja,et al.  Damage and Failure of Composite Materials , 2012 .

[9]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[10]  E. Jaynes Probability theory : the logic of science , 2003 .

[11]  M. Rigamonti,et al.  Tension fatigue analysis and life prediction for composite laminates , 1989 .

[12]  Kenneth Reifsnider,et al.  Characterization and Analysis of Damage Mechanisms in Tension-Tension Fatigue of Graphite/Epoxy Laminates , 1984 .

[13]  Enrico Zio,et al.  Fatigue crack growth estimation by relevance vector machine , 2012, Expert Syst. Appl..

[14]  A. Hosoi,et al.  Quantitative evaluation of fatigue damage growth in CFRP laminates that changes due to applied stress level , 2011 .

[15]  R. Jamison The role of microdamage in tensile failure of graphite/epoxy laminates☆ , 1985 .

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

[17]  Hugh Shercliff,et al.  Failure processes in composite materials: getting physical , 2006 .

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

[19]  Tsu-Wei Chou,et al.  Statistical analysis of multiple fracture in 0°/90°/0° glass fibre/epoxy resin laminates , 1983 .

[20]  John A. Nairn,et al.  2.12 – Matrix Microcracking in Composites , 2000 .

[21]  Serge Abrate,et al.  Matrix cracking in laminated composites: A review , 1991 .

[22]  Joel P. Conte,et al.  A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level , 2014 .

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

[24]  Charles R. Farrar,et al.  A reliability-based framework for fatigue damage prognosis of composite aircraft structures , 2012 .

[25]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[26]  Arnaud Doucet,et al.  An overview of sequential Monte Carlo methods for parameter estimation in general state-space models , 2009 .

[27]  Abhinav Saxena,et al.  Developing IVHM Requirements for Aerospace Systems , 2013 .

[28]  Kenneth Reifsnider,et al.  Analysis of fatigue damage in composite laminates , 1980 .

[29]  Charles E. Harris,et al.  Internal State Variable Approach for Predicting Stiffness Reductions in Fibrous Laminated Composites with Matrix Cracks , 1989 .

[30]  Stephen W. Tsai,et al.  A General Theory of Strength for Anisotropic Materials , 1971 .

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

[32]  Christian Boller,et al.  Fatigue in aerostructures—where structural health monitoring can contribute to a complex subject , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  Enrico Zio,et al.  Monte Carlo-based filtering for fatigue crack growth estimation , 2009 .

[34]  E. Morozov,et al.  Mechanics and analysis of composite materials , 2001 .

[35]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[36]  M. Alder,et al.  Degradation monitoring of impact damaged carbon fibre reinforced polymers under fatigue loading with pulse phase thermography , 2014 .

[37]  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.

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

[39]  Wim Van Paepegem,et al.  Fatigue damage modeling of fibre-reinforced composite materials: Review , 2001 .

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

[41]  Andrew Hess,et al.  The Joint Strike Fighter (JSF) PHM and the Autonomic Logistic Concept: Potential Impact on Aging Aircraft Problems , 2003 .

[42]  Kai Goebel,et al.  Model-Based Prognostics With Concurrent Damage Progression Processes , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[43]  J. Nairn Fracture Mechanics of Composites With Residual Thermal Stresses , 1997 .

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

[45]  Hiroyuki Kawada,et al.  High-cycle fatigue characteristics of quasi-isotropic CFRP laminates over 108 cycles (Initiation and propagation of delamination considering interaction with transverse cracks) , 2010 .

[46]  P. Paris A rational analytic theory of fatigue , 1961 .

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

[48]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[49]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[50]  Nobuo Takeda,et al.  Initiation and growth of delamination from the tips of transverse cracks in CFRP cross-ply laminates , 1994 .