Wavelet Transform Modulus Maxima Decay Lines

An important task in structural health monitoring (SHM) is that of damage detection under varying environmental and operational conditions. Structures, under varying environmental conditions, change their mass, elasticity and damping properties whereas changing operational conditions cause changes to excitations. A damage detection methodology implemented in these circumstances faces serious challenges since changes to structural behaviour imparted by environmental or operational conditions could be wrongly attributed to damage. The part of a damage detection decision algorithm that removes environmental and operational effects is called normalization. In this chapter a normalization methodology that is based on the similarity between continuous wavelet transform maxima decay lines is presented. This methodology is implemented on both simulated and experimental data. Simulated data were obtained from a three degree of freedom system. Varying environmental conditions were simulated by temperature dependent stiffness parameters and operating conditions by changing the colour of random excitation. Experimental data were obtained from damaged cantilever beams that were subjected to random excitations of different colour and varying temperatures.

Nonlinear Ultrasonics for Early Damage Detection

Structural Health Monitoring (SHM) is an emerging discipline that aims at improving the management of the life cycle of industrial components. The scope of this chapter is to present the integration of nonlinear ultrasonics with the Bayesian inverse problem as an appropriate tool to estimate the updated health state of a component taking into account the associated uncertainties. This updated information can be further used by prognostics algorithms to estimate the future damage stages. Nonlinear ultrasonics allows an early detection of damage moving forward the achievement of reliable predictions, while the inverse problem emerges as a rigorous method to extract the slight signature of early damage inside the experimental signals using theoretical models. The Bayesian version of the inverse problem allows measuring the underlying uncertainties, improving the prediction process. This chapter presents the fundamentals of nonlinear ultrasonics, their practical application for SHM, and the Bayesian inverse problem as a method to unveil damage and manage uncertainty.

Structure Impact Localization Using Emerging Artificial Intelligence Algorithms

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks. In theory, this algorithm is able to provide good generalization capability at extremely fast learning speed. Comparative studies of benchmark function approximation problems revealed that ELM can learn thousands of times faster than conventional neural network (NN) and can produce good generalization performance in most cases. Unfortunately, the research on damage localization using ELM is limited in the literature. In this chapter, the ELM is extended to the domain of damage localization of plate structures. Its effectiveness in comparison with typical neural networks such as back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) is illustrated through experimental studies. Comparative investigations in terms of learning time and localization accuracy are carried out in detail. It is shown that ELM paves a new way in the domain of plate structure health monitoring. Both advantages and disadvantages of using ELM are discussed.

New Features for Damage Detection and Their Temperature Stability

This chapter is devoted to present novel techniques in Structural Health Monitoring (SHM). These techniques are based on different statistical and signal processing methods that are used in other fields but their performance and capability in SHM is presented and tested for the first time in this work. This work is dedicated to the first level of SHM, which might be considered the main and most important level. Piezoceramic (PZT) devices are chosen in this work to capture the signals due to their special characteristics such as high performance, low energy consumption and reasonable price. Suggested techniques are tested on different laboratory and real scale test benchmarks. Moreover, this work considers the effect of environmental changes on performance of the presented techniques. This work shows that although those techniques have a significant result in normal conditions, their performance can be affected by any environmental discrepancy such as temperature change. As such, there is a vital need to consider their effect. In this work, temperature change is chosen, as it is one of the main environmental fluctuation factors.

Prognostics Design for Structural Health Management

The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.

Statistical Approach to Structural Damage Diagnosis under Uncertainty

This chapter presents a statistical methodology for structural damage diagnosis (detection, localization and estimation), in the context of continuous online monitoring. There are several sources of uncertainty such as physical variability, measurement uncertainty and model errors that affect structural damage diagnosis, and therefore, it may not be possible to precisely detect, localize, and estimate damage. Hence, a statistical approach can help to identify these sources of uncertainty, quantify their combined effect on diagnosis, and thereby, provide an estimate of the confidence in the results of diagnosis. Damage detection is based on residuals between nominal and damaged system-level responses, using statistical hypothesis testing whose uncertainty can be captured easily. Localization is based on the comparison of damage signatures derived from the system model. Both classical statistics-based methods and Bayesian statistics-based methods are investigated to quantify the uncertainty in all the three steps of diagnosis, i.e. detection, localization, and quantification. While classical statistics-based methods use the concept of least squares-based optimization, Bayesian methods make use of likelihood function and Bayes theorem. The uncertainties in damage detection, isolation and quantification are combined to quantify the overall uncertainty in diagnosis. The proposed methods are illustrated using the example of a structural frame.

Case-Based Reasoning for Stiffness Changes Detection in Structures

This chapter presents an expert monitoring algorithm approach to detect, locate and quantify stiffness variations in structures. The algorithm is based on pattern recognition and artificial intelligence techniques that emulate knowledge based on human reasoning. The expert system (ES) uses time-frequency information about dynamics of structure, which is processed by using discrete wavelet transform (DWT), self-organizing maps (SOM), case-based reasoning (CBR) and principal component analysis (PCA). In addition, two applications are considered in order to evaluate the effectiveness of vibration analysis methodology and CBR in damage detection. The first application (Camacho 2010) uses the environmental excitation to detect and quantify damage in a Mechanical UBC ASCE Benchmark. The second one (Sandoval 2010) uses a predesigned signal to detect geometric damages on a gas pipeline. In both cases, a finite element model (FEM) is used to simulate different damages scenarios, which correspond to stiffness variations in different location.

Structural Damage Assessment using an Artificial Immune System

Structural damage assessment methodologies allow providing knowledge about the current state of the structure. This information is important because allows to avoid possible accidents and perform maintenance tasks in the structure. This chapter proposes the use of an artificial immune system to detect and classify damages in structures by using data from a multi-actuator piezoelectric system that is working in several actuation phases. In a first step of the methodology, principal component analysis (PCA) is used to build a baseline model by using the collected data. In a second step, the same experiments under similar conditions are performed with the structure in different states (damaged or not). These data are projected into the different baseline models for each actuator, in order to obtain the damages indices and build the antigens. The antigens are compared with the antibodies by using an affinity function and the result of this process allows detecting and classifying damages.

Self-Healing Properties of Conventional and Fly Ash Cementitious Mortar, Exposed to High Temperature

Direct stress and sub-stress caused by fire, temperature variation and external loading in a structure are most important for the development of cracks. The chemical reactions of natural healing in the matrix was not been established conclusively. The most significant factor that influences the self-healing is the precipitation of calcium carbonate crystals on the crack surface. The mechanism which contribute autogenic healing are: (a) Continued hydration of cement at cracked surface as well as continued hydration of already formed gel and also inter-crystallization of fractured crystals; (b) blocking of flow path by water impurities and concrete particles broken from the crack surface due to cracking; (c) expansion of concrete in the crack flank (swelling) and closing of cracks by spalling of loose concrete particle are also reported as the sealing mechanism by researchers. The recovery of mechanical as well as physical property was discussed by different researchers. An experimental investigation was carried out to study the autogenic healing of fire damaged fly ash and conventional cementitious mortar samples subjected to steam followed by water curing at normal atmospheric pressure. The micro cracks are generated artificially by heating the 28 days aged mortar samples at 800 Deg. C. The effect of fly-ash replacing ordinary Portland cement by 0 and 20% was studied. Recovery of compressive strength and physical properties i.e. apparent porosity, water absorption, ultrasonic pulse velocity and rapid chloride ion penetration test confirm the self-healing of micro cracks. Such healing is more prominent for fly ash mortar mix. Optical as well as scanning electron microscopy With EDAX analysis and X-ray diffraction study of the white crystalline material formed in the crack, confirms formation of calcium carbonate.

An Implementation of a Complete Methodology for Wind Energy Structures Health Monitoring

In this chapter a complete methodology for a SHM damage detection solution is explained, and how this is validated in a laboratory tower model. Several methodologies are proposed for the typical process of SHM. Starting with sensor placement (the best possible sensor locations are found), selecting the more representative data, classifying the different environmental and operational conditions, applying a damage detection methodology, including sensor fault detection. The paradigm of damage detection can be tackled as a pattern recognition problem (comparison between the data collected from the structure without damages and the current structure in order to determine if there are any changes). There are lots of techniques that can handle the problem. In this work, accelerometer data is used to develop statistical data driven approaches for the detection of damages in structures. As the methodology is designed for wind turbines, only the output data is used to detect damage; the excitation of the wind turbine is provided by the wind itself or by the sea waves, being those unknown and unpredictable.