bluuebunny/crossref_metadata_2025_split huggingface dataset:
Directed Energy Sensors/Actuators
Authors: Blackshire James L. 
Published: September 2009

Abstract
Directed energy sensing methods have found increased use as inspection tools over the last several decades. To a large degree, this is due to the abundance of new energy sources and detectors, which span the entire electromagnetic spectrum. In this brief review article, the key underlying principles of directed energy sensing are presented with a special emphasis on recent developments related to material inspections, nondestructive evaluation, and structural health monitoring. For most applications, directed energy approaches are best classified by their wavelength/frequency, which along with incident energy/power levels determines how the directed energy interacts with the material of interest. In keeping with this theme, specific technology examples will be provided for short‐wavelength X ray, midwave visible, long‐wave infrared, and ultra‐long‐wave terahertz/microwave inspection approaches. The interested reader is encouraged to refer to the numerous cited articles and books for additional information and details, which are covered in the article.
Signal Processing for Damage Detection
Authors: Staszewski Wieslaw J. , Worden Keith 
Published: September 2009

Abstract
This article is intended to provide motivation for the study of signal processing methods in the context of Structural health monitoring (SHM). Apart from providing a brief discussion of the main areas of SHM in which signal processing is required in order to make progress, the article provides a roadmap for the signal processing part of the Encyclopedia and guides the reader to the more detailed expositions of the various topics.
Data Preprocessing for Damage Detection
Authors: Halfpenny Andrew 
Published: September 2009

Abstract
An effective structural health monitoring (SHM) system relies on good quality measurement data. All measured data are susceptible to noise or errors, which are introduced at various stages along the signal path. Some errors gradually develop as the system ages, while others are apparent only under certain operating conditions. In this article, we introduce the most common types of measurement errors and discuss methods for detecting and, where possible, removing them to recover the original measured signal.

The article begins with an overview describing stages along the signal path and the types of errors commonly introduced. We describe the most common configuration errors associated with SHM data acquisition and show how errors can be identified, remedied, or avoided through good system design. The term
noise
is used to describe an undesirable component within the signal. The article continues by categorizing the types of noise, identifying the cause of noise, and presenting methods of denoising measured data. We conclude by describing techniques that are particularly suited to the analysis of rotating machinery.
Cepstral Methods of Operational Modal Analysis
Authors: Randall Robert B. 
Published: September 2009

AbstractThe cepstrum is the inverse Fourier transform of the logarithmic spectrum. For SIMO (single input, multiple output) systems, the cepstrum of each output signal is the sum of the cepstra of the forcing function and the system properties. Where the input log spectrum is reasonably smooth and flat (impulsive or broadband random), the input is concentrated in the low part of the cepstrum and the high part can be curve fitted for the system poles and zeros. For minimum‐phase systems, the complex cepstrum can be derived from response autospectra only. The so‐called differential cepstrum has the advantage that it can be calculated from a time signal directly and also that its mathematical formulation allows the poles and zeros to be obtained using methods designed for fitting impulse response functions. For excitation by a series of (say burst random) pulses, the “mean differential cepstrum” gives a more accurate result. For MIMO (multiple input, multiple output) systems, which virtually always apply in practice, two approaches are currently being investigated: blind source separation to separate the response to one of the inputs (thus SIMO) and a matrix decomposition analogous to the mean differential cepstrum. The advantage of the cepstral techniques is the potential for obtaining scaled mode shapes because of obtaining zeros as well as poles.
Wavelet Analysis
Authors: Robertson Amy N. , Basu Biswajit 
Published: September 2009

AbstractWavelet analysis, as a time–frequency analysis tool, has shown great promise in recent years. The advantage of the wavelet transform over other time–frequency transforms is in its capabilities of flexible time windowing and computational efficiency. It is evidenced from the reported literature in this article that the wavelet transform has immense potential for application in the area of structural health monitoring (SHM). The theoretical foundations of continuous, discrete wavelet transform (DWT) and orthogonal wavelet transform have been presented. The construction of a multiresolution analysis (MRA)‐based fast algorithm for calculating the DWT and its relation to filtering of signals is discussed. Also presented is the concept of wavelet packets and a brief discussion on the choice of wavelets to suit applications. Examples are presented that show the applications of continuous, discrete, and orthogonal wavelets for the detection of damage by analysis of Lamb wave propagation, and the application of wavelet packets for the identification of stiffness variation in structural systems.
Model‐Based Statistical Signal Processing for Change and Damage Detection
Authors: Basseville Michèle 
Published: September 2009

AbstractHandling parameterized models for monitoring structures or processes is a natural approach to damage or fault detection and diagnosis. Modeling damages and faults as deviations in the parameter vector with respect to its nominal value in safe conditions calls for the use of statistical change detection and isolation methods. Such methods share the ability of handling noises and uncertainties with other statistical approaches.The purpose of this article is to describe the key elements in the design of change detection algorithms. Basic concepts (likelihood ratio, estimation, and innovation) are introduced. These tools involve a number of familiar operations (integration, averaging, sensitivity, adaptive thresholds, and windows). It is suggested that a statistical framework enlightens the meaning and increases the power of those operations. Vibration‐based structural health monitoring is addressed within this framework.
Statistical Pattern Recognition
Authors: Sohn Hoon , Oh Chang Kook 
Published: September 2009

AbstractStructural health monitoring (SHM) problems can often be cast in the context of a statistical pattern recognition paradigm. In this article, a suite of statistical pattern recognition tools applicable to various levels of SHM are introduced. First, multivariate analyses and dimensionality reduction techniques are introduced for effective feature extraction. Here, feature extraction is a step of mining features, which are sensitive to damage of interest, from measured raw signals. Secondly, training data are used to build a statistical model in the inference stage, and the model is used for subsequent decision making for classification and regression problems. If data are available from both undamaged and damaged systems, supervised learning techniques can be used to detect changes in system features extracted from these data that are indicative of damage. If data are only available from the undamaged system, unsupervised learning approaches to subsequent data can be used to identify damage. Finally, ongoing research efforts to address data normalization are presented. Data normalization is a process of separating signal changes caused by operational and environmental variations of the system from those due to structural damage.
Optimization Techniques for Damage Detection
Authors: Worden Keith , Staszewski Wieslaw , Manson Graeme , Ruotulo Aldo , Surace Cecilia 
Published: September 2009

AbstractThis article is concerned with techniques for optimization problems in the context of structural health monitoring. Many techniques are available for solving various classes of problems, including the computationally demanding combinatorial optimization problems which arise in principled approaches to feature selection or sensor optimization. This article briefly describes the classical, gradient‐based approaches that are well represented in the mathematical literature, and then goes on to discuss, in much more detail, the heuristic methods inspired by physics and biology, which have (comparatively) recently proved to be very powerful for combinatorial problems.
Finite Elements: Modeling of Piezoceramic and Magnetostrictive Sensors and Actuators
Authors: Gopalakrishnan Srinivasan 
Published: September 2009

AbstractIn this article, finite element modeling of smart structures with built‐in smart sensors and actuators is presented. Three different smart material sensors/actuators, namely, the piezoelectric (PZT—lead–zirconate–titanate), magnetostrictive (Terfenol‐D), and piezofiber composite material patches used as sensors/actuators, are considered for modeling in this article. The first part of the article briefly describes the constitutive models for these materials (which is dealt extensively in previous articles in this section), which is followed by the finite element formulation of 1‐D and 2‐D finite elements. Also, finite element model for a thin‐walled beam element is presented. Some numerical examples using the formulated element are presented to demonstrate the abilities of the formulated element.
Modeling for Detection of Degraded Zones in Metallic and Composite Structures
Authors: Ostachowicz Wiesław , Krawczuk Marek 
Published: September 2009

AbstractThis paper presents the state of the art of modeling and detection of damages in metallic and composite structures. The continuous, discrete‐continuous, and discrete models available in the literature are described. Also, the influence of damages on vibration behavior of structures is presented, along with possibilities of damage detection based on changes in dynamic characteristics of structures.
Modeling of
L
amb Waves in Composite Structures
Authors: Chakraborty Abir 
Published: September 2009

Abstract
An overview of the modeling aspects of Lamb waves in composite structure is presented. Lamb wave modes are computed using two methods: one by directly solving the two‐dimensional elasticity equation and the second by using various higher order beam theories. The performance of different beam theories is discussed in comparison to the solution of the original equation. Relative merits and demerits of each method are discussed. Along with the mode computation, a procedure is also outlined to obtain the simulation of propagating Lamb wave modes. The method also includes the solution of the governing elastodynamic equation for layered media subjected to arbitrary loading and boundary conditions. Lamb wave modes are provided for unidirectional laminated plies, and the procedure for general ply stacking is outlined.
Nondestructive Evaluation/Nondestructive Testing/Nondestructive Inspection (NDE/NDT/NDI) Sensors—Eddy Current, Ultrasonic, and Acoustic Emission Sensors
Authors: Blodgett Mark , Lindgren Eric , Sathish Shamachary , Jata Kumar V. 
Published: September 2009

AbstractMaterial state characterization is often performed using nondestructive evaluation (NDE) techniques with an objective to either understand or evaluate the microstructural state of the material or indirectly determine the damage that may have occurred in the material during manufacturing or service. Understanding material state is crucial, since material properties define the material's ability to perform its designed function. A number of nondestructive evaluation/nondestructive testing/nondestructive inspection (NDE/NDT/NDI) sensors are available to measure properties of a material. Basic laws of physics dictate the function of the sensor. This article attempts to explain the physics behind eddy current, ultrasonic, and acoustic emission sensors routinely used in NDI of aircraft structures.
Eddy‐Currentin situSensors forSHM
Authors: Goldfine Neil , Zilberstein Vladimir , Schlicker Darrell , Grundy Dave 
Published: September 2009

AbstractThis article describes capabilities of surface‐mounted and embedded eddy‐current sensors for onboard structural health monitoring (SHM), including detection and monitoring of damage as well as monitoring of usage state variables such as stresses and temperatures. The focus is on flexible, model‐based SHM sensors such as MWM‐Arrays, which are placed at selected locations to provide enhanced observability of damage and usage state variables as well as enable diagnostics—specifically to support life management decisions.
Miniaturized Sensors Employing Micro‐ and Nanotechnologies
Authors: Loh Kenneth J. , Lynch Jerome P. 
Published: September 2009

AbstractMicroelectromechanical systems (MEMSs) represent a revolutionary approach to the miniaturization of sensors designed for structural health monitoring (SHM) applications. The characteristic of MEMS sensors is their size, which can be on the order of a few micrometers in scale. Such sensors also enjoy low fabrication costs, low power demands, and high sensitivity. In this article, the application of MEMS sensors, namely, accelerometers, strain sensors, acoustic emission (AE) sensors, corrosion sensors, and wireless interdigital transducers are described. While MEMS has made major inroads into SHM applications, future miniaturization of sensors will be derived from the nanotechnology domain. Nanotechnology and its associated tools offer engineers an unprecedented opportunity to design sensing materials at atomistic length‐scales; the results are multifunctional materials capable of sensing their response to loads.
Design of Active Sensor Network and Multilevel Data Fusion
Authors: Wang Xiaoming , Su Zhongqing 
Published: September 2009

Abstract
Design of a sensing network, and subsequently, the interpretation of data acquired from sensors, are two critical issues in structural health monitoring (SHM). It is understood that the process to identify structural health status is basically an inverse problem, often mathematically ill‐posed. This article has the goal of introducing the active sensor network, a spatially distributed sensing structure, designed to deal with the problem of SHM. Data fusion, especially multilevel data fusion, is then presented to face the challenges in data interpretation, which can be considered as a process to establish a posterior belief about a set of propositions like structural damage events on the basis of a set of prior beliefs that are possessed by sensors. A data fusion process is utilized to increase the robustness and reliability of structural condition identification algorithms by reducing imprecision, uncertainties, and incompleteness. There is a wide range of approaches in data fusion, including the algorithms based on voting scheme, Bayesian theory, Dempster–Schafer rules, fuzzy inference, and artificial neural network.
On the Way to Autonomy: the Wireless‐interrogated and Self‐powered “Smart Patch” System
Authors: Galea Stephen C. , Van der Velden Stephen , Moss Scott , Powlesland Ian 
Published: September 2009

Abstract

Composite patches bonded to defective aircraft structures are a recognized cost‐effective repair or reinforcement technique for many types of structural problems, such as metallic cracking, repairing corrosion damage, and reducing fatigue strain at structural hot spots. However, certification concerns limit the application of composite bonded repairs in critical components. For certification and management of repairs to critical structure, the
smart patch
approach may be a useful approach from the airworthiness perspective in facilitating certification. The
smart patch
consists of a number of
in situ
sensors used to monitor the structural condition (health or well‐being) of the patch system and the status of the remaining damage in the parent structure. In summary the
smart patch
is an
in situ
structural health monitoring (SHM) technology applied to a composite bonded repaired structure. The demonstration of the
smart patch
on an operational aircraft offers an excellent vehicle to demonstrate autonomous SHM technology in an operational environment.

This article describes the development, evaluation, and implementation of the self‐powered wireless
smart patch
system, including issues such as system design, functionality and certification testing, and installation. This system had no primary power source (i.e., battery) and was powered instead by energy harvested from the local strain environment. It was wirelessly interrogated using a magnetic transceiver and employed piezoelectric elements for both powering and health monitoring functions. The article gives a brief overview of the various available power‐harvesting techniques considered and discusses several key issues that arose when designing and developing the self‐powered
smart patch
system, namely, (i) demand—power requirements, (ii) supply—energy generation from vibration or strain‐based sources, and (iii) conversion—issues and efficiencies associated with energy conversion from mechanical to electrical energies. Flight data from the system and lessons learned during the program are also presented.
Stanford Multiactuator–Receiver Transduction (
SMART
)
L
ayer Technology and Its Applications
Authors: Qing Xinlin P. , Beard Shawn J. , Kumar Amrita , Li Irene , Lin Mark , Chang Fu‐Kuo 
Published: September 2009

Abstract
This article summarizes the latest development efforts on the Stanford multiactuator‐receiver transduction (SMART) Layer technology used for structural health monitoring (SHM) for various applications. The SMART Layer consists of a network of thin piezoelectric sensors and actuators that are embedded into a dielectric film through an electronic circuit printing and lamination technique. The SMART Layer provides a viable and cost‐effective means for monitoring the health of structures of various configurations and sizes. The SMART Layer is very versatile, and by coupling it with appropriate diagnostic software and hardware, the same layer can be developed into a passive SHM system or an active SHM system. The passive and active systems are specifically designed for detection of impacts on a structure; diagnosis of cracks in metallic structures and impact damage and debonds in composite structures. Recent advances in techniques for fabricating the SMART Layers, as well as methods for integrating the layers into structures, are illustrated and examples are presented. The mechanical and electrical properties of the layer are highlighted, including the effect of embedment of the layer on the integrity of composite structures. Other practical issues, such as the environmental effect on the performance of the layer and the self‐diagnostics of the embedded network, are also discussed.
Design Principles for Civil Structures
Authors: Karbhari Vistasp M. 
Published: September 2009

Abstract
Although the term structural health monitoring (SHM) has gained prominence recently, its basis and motivation can be traced to the very earliest endeavors of mankind to conceptualize, construct, worry about deterioration, and then attempt to repair (or otherwise prolong the life) of a structure. Thus, it represents an attempt at deriving knowledge about the actual condition of a structure, or system, with the aim of not just knowing that its performance may have deteriorated, but rather to be able to assess remaining performance levels and life. In the case of civil structures where the expected service life is high (30–75+ years), where structures are repeated but are often one of a kind, and which are expected to continue performing in harsh and changing outdoor environments with low levels of inspection and maintenance, the needs of an SHM system are somewhat different from those used in aircraft and with machinery. The design principles are intrinsically related to the specifics of the structure including configuration and necessity of considering an outdoor environment.
Diagnosing Offshore Machines and Power Plants Using Vibration Methods
Authors: Grzadziela Andrzej 
Published: September 2009

AbstractCurrently, vibration methods of diagnosing machines and power plants are popular in offshore technology. They provide a lot of information about the dynamic behavior of rotating machines. This article presents the philosophy of operational decision according to measured vibration parameters. The main purpose of the tests is to determine the execution characteristics of vibration parameters in the functioning of main crankshaft or rotor revolution, loads, and operation time. Dissipation of energy in rotating machines is displayed as a torque, revolutions, temperature, gas flow, and vibration. Vibrations are connected to rotor imbalance, resonances, oversize of tolerated axis slope of shafts, abrade of blade tips with the inner roller, wear of axis and radial bearings, asymmetry of springiness and damping characteristics of rotors and their parts, and irregularity of gas‐flow forces. Emission of vibration brings a lot of information including opinion of the technical state of a machine. Measurements of vibration, their identification, classification, and mathematical analysis, including trend function, bring information on the actual technical state and allow predicting wear process of machines in the future. This article presents an application of the vibration method for diagnosing the following typical offshore equipment: reciprocating gas compressors, power plants equipped with gas turbines engines, and offshore tugs' main engines.
Landfills
Authors: Münnich Kai , Bauer Jan , Fricke Klaus 
Published: September 2009

Abstract
Landfills are very large and complex engineering structures, which have to be functionally stable as long as they represent a potential danger to the environment. From the multitude of potential risks, only fluid emissions and mechanical failure are considered in this article.

For landfill owners and operators, the need for monitoring of landfills is often only combined with the costs resulting from it. The experiences show that monitoring during the different stages of the lifespan of landfills is necessary to get an idea of the long‐term behavior of the landfill under the specific conditions of the site. This knowledge is important in view of prevention of uncontrolled emissions as well as large‐scale mechanical failure of the landfill structure. In this article, new measurement devices for monitoring leachate discharge and the deformation behavior of the landfill are presented, as well as the results of
in situ
measurements. The better spatiotemporal resolution allows a better understanding of the different processes occurring in the landfill.
SHMand Lifetime Management of Industrial Piping Systems
Authors: Schubert Frank , Frankenstein Bernd , Klesse Thomas , Kerkhof Klaus , Schuler Xaver , Friedmann Herbert , Henkel Fritz‐Otto , Wenzel Helmut 
Published: September 2009

AbstractIn this article, a structural health monitoring system for industrial piping systems is presented that combines global vibration monitoring and local guided wave monitoring within a multichannel and multipurpose measurement system. While vibration monitoring is able to characterize the global condition of the structure due to supports and dampers, the guided wave module is able to find small cracklike defects and corrosive material degradation in crucial error‐prone parts of the components. It can therefore be expected that a combination of the “global” and “local eye” will lead to a monitoring system with significantly increased performance and reliability. At the end of the article, the development of a joint decision support system embedded in an overall lifetime management for industrial piping systems is also described in detail.