Vibration-Based Techniques for Damage Detection and Localization in Engineering Structures

The following sections are included:

• Preface
• About the Editors
• Contents

This chapter poses the damage detection for civil, mechanical, and aerospace structures in the context of a pattern recognition paradigm for structural health monitoring (SHM), where machine learning algorithms are essential to learn the structural behavior from experience, following the same principle of the human brain. These algorithms are especially relevant in cases where the damage-sensitive features extracted from the structural responses are affected by changes caused by operational and environmental variability and changes caused by damage. State-of-the-art machine learning algorithms are presented based on Mahalanobis squared distance (MSD), Gaussian mixture models (GMMs), principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN); the bioinspired algorithms are highlighted as promising algorithms to overcome some of the limitations of the more traditional ones. All the algorithms present different working principles and seek to generalize the normal structural condition in order to detect deviations, from the baseline condition, associated with damage. The applicability of the chosen algorithms for damage detection will be tested on standard data sets from the Z-24 Bridge in Switzerland. These data sets are unique as they combine 1-year monitoring from the baseline condition, when influenced by extreme operational and environmental variability, with realistic damage scenarios.

This chapter studies the application of data-driven methods and specifically principal component analysis (PCA) and singular spectrum analysis (SSA) for purposes of damage assessment in structures and machinery. In this study, data analysis methods PCA and SSA are applied to the measured vibration signals in order to extract information about the state of the structure/machinery and the presence of a fault in it. Two applications are offered, one for damage assessment on a wind turbine blade and another one for fault diagnosis in rolling element bearings. The results demonstrate strong capabilities of the investigated methodology for both structural damage detection and rolling element fault diagnosis. Eventually, a discussion about the capabilities of the studied methodology and the way forward regarding extending its capabilities and applications is offered.

As an attempt towards damage detection in composites, in this chapter, an experimental investigation is presented aiming at extending the current understanding of how delamination will affect the vibration characteristics of carbon fiber-reinforced plastics (CFRP). Different percentages of delamination have been applied artificially on CFRPs, and experimental modal analysis has been performed for both natural frequency and modal loss factor parameters. It is a well-established fact that, due to the global nature of lower modes’ natural frequencies, small defects have small effects on these modal parameters and hence, generally speaking, natural frequency is not a good feature for damage indication. On the other hand, modal loss factor can be considered as a good damage index (DI), especially, if the healthy structure is of low damping characteristic. However, the problem with modal loss factor is that it is hard to identify reliable values for this parameter and usually identified values have high scatter. In order to be able to identify reliable modal loss factors, the modal parameter extraction techniques with high order of accuracy in damping identification, i.e. circle fit (CF) and line fit (LF) methods, are used, leading to the rationalization process being optimized, resulting in the extended line fit method, (ELFM). Using ELFM, it has been shown that both natural frequency and modal loss factor have changed due to delamination. While, as expected, natural frequency has experienced insignificant changes, modal loss factor has proved to be a highly sensitive indicator, undergoing major changes even at initial damage stages. Modal damping mechanisms and their relationship with mode shapes have been examined. The results reveal that delamination severity can be detected using modal loss factor variations.

In damage detection, an important aspect is the accurate frequency evaluation which permits observing the modal parameter changes at the earliest stage. The frequency resolution improvement, achieved by extending the time interval of the analysis, is in the case of standard frequency estimation the key to increasing the accuracy of the study. This is not always possible due to the rapid decay of higher-order modes. Many methods to improve frequency readability are nowadays available in the literature, such as those increasing the spectral lines density or those based on several-point interpolation. We debate herein the limitations of the actual methods and introduce an advanced algorithm that uses for interpolation three peaks from three different spectra, achieved from the acquired signal that is stepwise cropped. Processing the signal in this way allows identifying minor natural frequency changes, thus allowing for the observation of damage occurrence in the earliest stage. The algorithm was successfully tested against generated and real signals.

This chapter describes the application of shearography, which is a non-contact, full-field, and high-resolution optical method, for the measurement of modal response of beams and plates and subsequent damage localization. A review of literature on shearography and related interferometric techniques for vibration analysis is also presented. Since shearography is based on speckle interferometry, the physical and mathematical bases of speckle phenomenon and wave interference are described. The main techniques available to evaluate the phase maps obtained from the speckle patterns are considered and illustrated. Different processes of filtering and unwrapping the phase maps, which must be carried out in order to obtain the gradients of the modal displacement fields and thus the modal rotation field, are also presented in this chapter. Case studies of damage localization using measurements of modal response with two distinct shearography systems are also reported and discussed. Both single and multiple damages in aluminum beams are created using a saw or a milling machine. In order to test the accuracy and effectiveness of the damage localization approach, several amounts of damages are considered. It is found out that very small damage can be localized using higher order derivatives of the mode shapes, namely the fourth order.

This chapter presents an overview of damage detection methods that make use of mode shapes. Both classic and novel approaches to mode shape analysis are presented. In the latter, two data sets (in the reference state and the tested state) are compared to extract information about potential changes caused by damage. In the former case, damage indices are based on various signal processing methods in which only one set of data is used for detecting and localizing any possible damage in a tested structure.

This chapter reviews and compares some of the most known methods of damage identification, in the frequency domain, which use operational deflection shapes (ODSs) built from the frequency response functions (FRFs). Two new methods of damage identification, in the time domain, using (ODSs) built from the time responses are also analyzed. To assess the performance of the different indicators, two numerical tests are undertaken with a finite element model (FEM) model, of a cantilever beam, to evaluate their detection and localizing ability.

The following section is included:

• Index