This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based versus non-model-based methods, and linear versus nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current, and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.

A summary review of vibration-based damage identification methods

This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book. In the process of providing the historical overview and summarising the SPR paradigm, the subsequent chapters in this book are cited in an effort to show how they fit into this overview of SHM. In the conclusions are stated a number of technical challenges that the authors believe must be addressed if SHM is to gain wider acceptance.

An introduction to structural health monitoring

Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.

A review of structural health monitoring literature 1996-2001

Guided Lamb waves for identification of damage in composite structures: A review

Past, present and future of nonlinear system identification in structural dynamics

http://www.researchgate.net/profile/Sebastien_Grondel/publication/229888941_Structural_Health_Monitoring_Evaluation_Tests/links/0912f510bb4be1d023000000.pdf

Health Monitoring of Aerospace Structures

Summary chapter and guidelines on nonlinear procedures: Flow diagrams. Why not dynamical systems theory. Summary of linear system theory: Continuous-time. Discrete-time. Dynamic testing of linear and nonlinear structures: Simple procedures for detecting nonlinearity in dynamic testing. Sine, Chirp, Random, Impulse etc. Linearisation. Correlations-coherence. FRFs of linear and nonlinear systems: Harmonic balance. Averaging methods. Nyquist plots. Carpet plots. MDOF systems. Hilbert transform - a practical approach: Definition in terms of odd/even functions. Time-frequency domain definitions. Computation - fast method. Correction terms. Principal component analysis. Corehence. Damping estimation (Khalid's work). Linearisation from random testing. Spectral moments. Hilbert transform - a complex analytical approach: Contour integrals. Titchmarsh's theorem. Artificial noncausality. Correction for asymptotic behaviour. Viscous v. Hysteretic damping - exponential integrals. Pole-zero decomposition - estimation without truncation. Restoring force surfaces and direct parameter estimation: Masri/Caughey theory. Link models/Khalids approach. Application requirements - integration/differentiation of data. Least-squares estimation: Normal equations. Orthogonal estimator. SVD. Recursive LS. - forgetting factors. Discrete-time methods: NARMAX. AVD. Model validity. Functional series: Volterra series. Existence, uniqueness, convergence. Connection with Green's functions - calculation - symmetries. Fliess/Lamnabhi power series approach. Wiener series - high-dimensional correlations - Volterra limit. Higher order FRFs/Transfer functions: Harmonic probing. Interpretaton. SDOF/MDOF systems. Hypercurve fitting (S. Gifford). Convergence revisited (Dr Lee). Neural networks: Multi-layer perceptrons. Radial basis functions. Modelling nonlinear systems. Dynamics neurons. Networks as nonlinear dynamical systems. Classification of nonlinear systems: Pattern recognition/feature extraction. Wigner-Ville distribution. Wavelet transform. Neural networks. FRFs for classification SDOF/MDOF. Nonlinear least-squares: Piecewise-linear systems. Hysteretic systems. Yar/Hammond - GA parameter estimation. Gradient descent - shock absorber model.

G.R. Tomlinson

Papers

Health Monitoring of Aerospace Structures

Nonlinearity in Structural Dynamics: Detection, Identification and Modelling

Theoretical characteristics of the vibration of sandwich plates with in-plane negative poisson's ratio values

Theoretical study of the effects of nonlinear viscous damping on vibration isolation of sdof systems

Comparisons between harmonic balance and nonlinear output frequency response function in nonlinear system analysis