Charles R. Farrar

Los Alamos National Laboratory, Los Alamos, California, United States

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Publications (332)199.17 Total impact

  • Source
    Michele Pasquali, Walter Lacarbonara, Charles R. Farrar
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    ABSTRACT: In this work, the delamination detection procedure presented in part I is experimentally investigated [1] Attention is placed on the through-the-thickness propagation direction of composite laminates undergoing delaminations. As observed in the numerical tests, a precise correlation between the delamination position and the variations of the time of flight (ToF) of primary (P) and secondary (S) waves is found. A substantial modulation of the power spectral density (PSD) of the acquired output signals is recorded in the case of delaminations close to the surface onto which the actuator/sensor transducers are bonded. The experimental validation of the proposed SHM procedure is carried out through extensive testing on different types of isotropic and composite specimen. A good agreement between the experimental results and the theoretical predictions shown in part I is found, together with determination of limitations on the delamination position offset with respect to the actuator/sensor pair position.
    Composite Structures 05/2015; DOI:10.1016/j.compstruct.2015.05.042 · 3.32 Impact Factor
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    M. Pasquali, C.J. Stull, C.R. Farrar
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    ABSTRACT: Info-Gap Decision Theory is adopted to assess the robustness of a technique aimed at identifying the optimal excitation signal to be used for active sensing approaches to damage detection. Here the term “active sensing” refers to procedures where a known input is applied to the structure to enhance the damage detection process. Given limited system response measurements and ever-present physical limits on the level of excitation, the ultimate goal of the mentioned technique is to improve the detectability of damage by increasing the difference between measured outputs of the undamaged and damaged systems. In particular, a two degree-of-freedom mass–spring–damper system characterized by the presence of a nonlinear stiffness is considered. Uncertainty is introduced to the system in the form of deviations of its parameters (mass, stiffness, damping ratio) from their nominal values. Variations in the performance of the mentioned technique are then evaluated both in terms of changes in the estimated difference between the responses of the damaged and undamaged systems and in terms of deviations of the identified optimal input signal from its nominal estimation. Finally, plots of the performances of the analyzed algorithm for different levels of uncertainty are obtained, enabling a clear evaluation of the risks connected with designing excitation signals for damage detection, when the parameters that dictate system behavior (e.g. stiffness, mass) are poorly characterized or improperly modeled.
    Mechanical Systems and Signal Processing 01/2015; s 50–51:1–10. DOI:10.1016/j.ymssp.2014.05.038 · 2.47 Impact Factor
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    ABSTRACT: In the Structural Health Monitoring of bridges, the effects of the operational and environmental variability on the structural responses have posed several challenges for early damage detection. In order to overcome those challenges, in the last decade recourse has been made to the statistical pattern recognition paradigm based on vibration data from long-term monitoring. This paradigm has been characterized by the use of purely data-based algorithms that do not depend on the physical descriptions of the structures. However, one drawback of this procedure is how to set up the baseline condition for new and existing bridges. Therefore, this paper proposes an algorithm with a Bayesian approach based on a Markov-chain Monte Carlo method to cluster structural responses of the bridges into a reduced number of global state conditions, by taking into account eventual multimodality and heterogeneity of the data distribution. This approach stands as an improvement over the classical maximum likelihood estimation based on the expectation–maximization algorithm. Along with the Mahalanobis squared-distance, this approach permits one to form an algorithm able to detect structural damage based on daily response data even under abnormal events caused by temperature variability. The applicability of this approach is demonstrated on standard data sets from a real-world bridge in Switzerland, namely the Z-24 Bridge. The analysis suggests that this algorithm might be useful for bridge applications because it permits one to overcome some of the limitations posed by the pattern recognition paradigm, especially when dealing with limited amounts of training data and/or data with nonlinear temperature dependency.
    Engineering Structures 12/2014; 80:1–10. DOI:10.1016/j.engstruct.2014.08.042 · 1.77 Impact Factor
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    ABSTRACT: The goal of this work is to develop a new autonomous capability for remotely deploying precisely located sensor nodes without damaging the sensor nodes in the process. Over the course of the last decade there has been significant interest in research to deploy sensor networks. This research is driven by the fact that the costs associated with installing sensor networks can be very high. In order to rapidly deploy sensor networks consisting of large numbers of sensor nodes, alternative techniques must be developed to place the sensor nodes in the field. To date much of the research on sensor network deployment has focused on strategies that involve the random dispersion of sensor nodes [1]. In addition other researchers have investigated deployment strategies utilizing small unmanned aerial helicopters for dropping sensor networks from the air. [2]. The problem with these strategies is that often sensor nodes need to be very precisely located for their measurements to be of any use. The reason for this could be that the sensor being used only have limited range, or need to be properly coupled to the environment which they are sensing. The problem with simply dropping sensor nodes is that for many applications it is necessary to deploy sensor nodes horizontally. In addition, to properly install many types of sensors, the sensor must assume a specific pose relative to the object being measured. In order to address these challenges we are currently developing a technology to remotely and rapidly deploy precisely located sensor nodes. The remote sensor placement device being developed can be described as an intelligent gas gun (Figure 1). A laser rangefinder is used to measure the distance to a specified target sensor location. This distance is then used to estimate the amount of energy required to propel the sensor node to the target location with just enough additional energy left over to ensure the sensor node is able to attach itself to the target of interest. We are currently in the process of developing attachment mechanisms for steel, wood, fiberglass (Figure 2). In this demonstration we will perform a contained, live demo of our prototype pneumatic remote sensor placement device along with some prototype sensor attachment mechanisms we are developing.
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    ABSTRACT: The goal of this work is to develop a new autonomous capability for remotely deploying precisely located sensor nodes without damaging the sensor nodes in the process. Over the course of the last decade there has been significant interest in research to deploy sensor networks. This research is driven by the fact that the costs associated with installing sensor networks can be very high. In order to rapidly deploy sensor networks consisting of large numbers of sensor nodes, alternative techniques must be developed to place the sensor nodes in the field. The goal of this work is to develop a new autonomous capability for remotely deploying precisely located sensor nodes without damaging the sensor nodes in the process. Over the course of the last decade there has been significant interest in research to deploy sensor networks. This research is driven by the fact that the costs associated with installing sensor networks can be very high. In order to rapidly deploy sensor networks consisting of large numbers of sensor nodes, alternative techniques must be developed to place the sensor nodes in the field. To date much of the research on sensor network deployment has focused on strategies that involve the random dispersion of sensor nodes [1]. In addition other researchers have investigated deployment strategies utilizing small unmanned aerial helicopters for dropping sensor networks from the air. [2]. The problem with these strategies is that often sensor nodes need to be very precisely located for their measurements to be of any use. The reason for this could be that the sensor being used only have limited range, or need to be properly coupled to the environment which they are sensing. The problem with simply dropping sensor nodes is that for many applications it is necessary to deploy sensor nodes horizontally. In addition, to properly install many types of sensors, the sensor must assume a specific pose relative to the object being measured. In order to address these challenges we are currently developing a technology to remotely and rapidly deploy precisely located sensor nodes. The remote sensor placement device being developed can be described as an intelligent gas gun (Figure 1). A laser rangefinder is used to measure the distance to a specified target sensor location. This distance is then used to estimate the amount of energy required to propel the sensor node to the target location with just enough additional energy left over to ensure the sensor node is able to attach itself to the target of interest. We are currently in the process of developing attachment mechanisms for steel, wood, fiberglass (Figure 2). In this demonstration we will perform a contained, live demo of our prototype pneumatic remote sensor placement device along with some prototype sensor attachment mechanisms we are developing.
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    ABSTRACT: In crack detection applications large sensor arrays are needed to be able to detect and locate cracks in structures. This paper analyzes different sensor shapes and layouts to determine the layout which provides the optimal performance. A “snaked hexagon” layout is proposed as the optimal sensor layout when both crack detection and crack location parameters are considered. In previous work we have developed a crack detection circuit which reduces the number of channels of the system by placing several sensors onto a common bus line. This helps reduce data and power consumption requirements but reduces the robustness of the system by creating the possibility of losing sensing in several sensors by a single broken wire. In this paper, sensor bus configurations are analyzed to increase the robustness of the bused sensor system. Results show that spacing sensors in the same bus out as much as possible increases the robustness of the system and that at least 3 buses are needed to prevent large segments of a structure from losing sensing in the event of a bus failure.
    SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring; 04/2014
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    ABSTRACT: In many applications of structural health monitoring (SHM) it is imperative or advantageous to have large sensor arrays in order to properly sense the state of health of the structure. Typically these sensor networks are implemented by placing a large number of sensors over a structure and running individual cables from each sensor back to a central measurement station. Data is then collected from each sensor on the network at a constant sampling rate regardless of the current timescales at which events are acting on the structure. These conventional SHM sensor networks have a number of shortfalls. They tend to have a large number of cables that can represent a single point of failure for each sensor as well as add significant weight and installation costs. The constant sampling rate associated with each sensor very quickly leads to large amounts of data that must be analyzed, stored, and possibly transmitted to a remote user. This leads to increased demands on power consumption, bandwidth, and size. It also taxes our current techniques for managing large amounts of data. For the last decade the goal of the SHM community has been to endow structures with the functionality of a biological nervous system. Despite this goal the community has predominantly ignored the biological nervous system as inspiration for building structural nervous systems, choosing instead to focus on experimental mechanics and simulation techniques. In this work we explore the use of a novel, bio-inspired, SHM skin. This skin makes use of distributed computing and asynchronous communication techniques to alleviate the scale of the data management challenge as well as reduce power. The system also periodically sends a 'heat beat' signal to provide state-of-health updates. This conductive skin was implemented using conductive ink resistors as well as with graphene-oxide capacitors.
    Smart Materials and Structures 04/2014; 23(5):055020. DOI:10.1088/0964-1726/23/5/055020 · 2.45 Impact Factor
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    ABSTRACT: With the increased interest in implementation of wind turbine power plants in remote areas, structural health monitoring (SHM) will be one of the key cards in the efficient establishment of wind turbines in the energy arena. Detection of blade damage at an early stage is a critical problem, as blade failure can lead to a catastrophic outcome for the entire wind turbine system. Experimental measurements from vibration analysis were extracted from a 9 m CX-100 blade by researchers at Los Alamos National Laboratory (LANL) throughout a full-scale fatigue test conducted at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC). The blade was harmonically excited at its first natural frequency using a Universal Resonant EXcitation (UREX) system. In the current study, machine learning algorithms based on Artificial Neural Networks (ANNs), including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs. This paper introduces such pattern recognition methods into the wind energy field and attempts to address the effectiveness of such methods by combining vibration response data with novelty detection techniques.
    Journal of Sound and Vibration 03/2014; 333(6):1833–1850. DOI:10.1016/j.jsv.2013.11.015 · 1.86 Impact Factor
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    ABSTRACT: The RAPTOR telescope systems are astronomical observatories that operate in remote locations in New Mexico searching for astrophysical transients called gamma-ray bursts. Their operating condition should remain at good levels in order to have accurate observations. Currently, the first component of the RAPTOR telescopes to fail is a capstan driving mechanism that operates in a run-to failure mode. The capstans wear relatively frequently because of their manufacturing material and can cause damage to other more expensive components, such as the drive wheels and the telescope optics. Monitoring the condition of these systems seems a reasonable solution since the unpredictable rate at which the capstans experience wear, in combination with the remote locations and high duty cycles of these telescope systems, make it unprofitable to choose a strategy of replacing the capstans at chosen intervals. Experimental tests of the telescope systems reported here recorded vibration signals during clockwise and counterclockwise rotations, similar to a motion known as "homing-sequence". The Empirical Mode Decomposition (EMD) method in combination with the Hilbert Transform (HT) and a new alternative method for the estimation of the instantaneous features of a signal that applies an energy tracking operator, called Teager-Kaiser Energy operator, and an energy separation algorithm to the data being analysed, are the time-frequency analysis methods used for analysis here.
    Key Engineering Materials 10/2013; 588:43-53. DOI:10.4028/www.scientific.net/KEM.588.43 · 0.19 Impact Factor
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    ABSTRACT: Structural health monitoring (SHM) systems will be one of the leading factors in the successful establishment of wind turbines in the energy arena. Detection of damage at an early stage is a vital issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of vibration analysis, extracted of a 9m CX-100 blade under fatigue loading. For analysis, machine learning techniques utilised for failure detection of wind turbine blades will be applied, like non-linear Neural Networks, including Auto-Associative Neural Network (AANN) and Radial Basis Function (RBF) networks models.
    Key Engineering Materials 10/2013; 588:166-174. DOI:10.4028/www.scientific.net/KEM.588.166 · 0.19 Impact Factor
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    ABSTRACT: This paper presents ongoing work by the authors to implement real-time structural health monitoring (SHM) systems for operational research-scale wind turbine blades. The authors have been investigating and assessing the performance of several techniques for SHM of wind turbine blades using piezoelectric active sensors. Following a series of laboratory vibration and fatigue tests, these techniques are being implemented using embedded systems developed by the authors. These embedded systems are being deployed on operating wind turbine platforms, including a 20-meter rotor diameter turbine, located in Bushland, TX, and a 4.5-meter rotor diameter turbine, located in Los Alamos, NM. The SHM approach includes measurements over multiple frequency ranges, in which diffuse ultrasonic waves are excited and recorded using an active sensing system, and the blades global ambient vibration response is recorded using a passive sensing system. These dual measurement types provide a means of correlating the effect of potential damage to changes in the global structural behavior of the blade. In order to provide a backdrop for the sensors and systems currently installed in the field, recent damage detection results for laboratory-based wind turbine blade experiments are reviewed. Our recent and ongoing experimental platforms for field tests are described, and experimental results from these field tests are presented. LA-UR-12-24691.
    Key Engineering Materials 06/2013; 558:364-373. DOI:10.4028/www.scientific.net/KEM.558.364 · 0.19 Impact Factor
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    ABSTRACT: The rapid deployment of satellites is hindered by the need to flight-qualify their components and the resulting mechanical assembly. Conventional methods for qualification testing of satellite components are costly and time consuming. Furthermore, full-scale vehicles must be subjected to launch loads during testing. The focus of this research effort was to assess the performance of Structural Health Monitoring (SHM) techniques to replace the high-cost qualification procedure and to localize faults introduced by improper assembly. SHM techniques were applied on a small-scale structure representative of a responsive satellite. The test structure consisted of an extruded aluminum spaceframe covered with aluminum shear plates, which was assembled using bolted joints. Multiple piezoelectric patches were bonded to the test structure and acted as combined actuators and sensors. Piezoelectric Active-sensing based wave propagation and frequency response function techniques were used in conjunction with finite element modeling to capture the dynamic properties of the test structure. Areas improperly assembled were identified and localized. This effort primarily focused on determining whether or not bolted joints on the structure were properly tightened.
    Measurement Science and Technology 05/2013; 24(7). DOI:10.1117/12.881984 · 1.35 Impact Factor
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    ABSTRACT: Over the course of the last few years, the Robot Operating System (ROS) has become a highly popular software framework for robotics research. ROS has a very active developer community and is widely used for robotics research in both academia and government labs. The prevalence and modularity of ROS cause many people to ask the question: “What prevents ROS from being used in commercial or government applications?” One of the main problems that is preventing this increased use of ROS in these applications is the question of characterizing its security (or lack thereof). In the summer of 2012, a crowd sourced cyber-physical security contest was launched at the cyber security conference DEF CON 20 to begin the process of characterizing the security of ROS. A small-scale, car-like robot was configured as a cyber-physical security “honeypot” running ROS. DEFFCON-20 attendees were invited to find exploits and vulnerabilities in the robot while network traffic was collected. The results of this experiment provided some interesting insights and opened up many security questions pertaining to deployed robotic systems. The Federal Aviation Administration is tasked with opening up the civil airspace to commercial drones by September 2015 and driverless cars are already legal for research purposes in a number of states. Given the integration of these robotic devices into our daily lives, the authors pose the following question: “What security exploits can a motivated person with little-to-no experience in cyber security execute, given the wide availability of free cyber security penetration testing tools such as Metasploit?” This research focuses on applying common, low-cost, low-overhead, cyber-attacks on a robot featuring ROS. This work documents the effectiveness of those attacks.
    SPIE Defense, Security, and Sensing; 05/2013
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    ABSTRACT: This article presents a performance optimization approach to incipient crack detection in a wind turbine rotor blade that underwent fatigue loading to failure. The objective of this article is to determine an optimal demarcation date, which is required to properly normalize active-sensing data collected and processed using disparate methods for the purpose of damage detection performance comparison. We propose that maximizing average damage detection performance with respect to a demarcation date would provide both an estimate of the true incipient damage onset date and the proper normalization enabling comparison of detection performance among the otherwise disparate data sets. This work focuses on the use of ultrasonic guided waves to detect incipient damage prior to the surfacing of a visible, catastrophic crack. The blade was instrumented with piezoelectric transducers, which were used in a pitch-catch mode over a range of excitation frequencies. With respect to specific excitation frequencies and transmission paths, higher excitation frequencies provided consistent detection results for paths along the rotor blade's carbon fiber spar cap, but performance fell off with increasing excitation frequency for paths not along the spar cap. Lower excitation frequencies provided consistent detection performance among all sensor paths.
    Journal of Intelligent Material Systems and Structures 02/2013; 25(5). DOI:10.1177/1045389X13510788 · 2.17 Impact Factor
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    ABSTRACT: Rocket engines are complex systems which usually operate under extreme physical conditions such as very high temperature and pressure, strong erosion, and high-density energy release. Mechanical and chemical complexity, long service lives, aging materials, and designs with small margins of safety are typical for space launch vehicle components including the engine. Furthermore, these components can be exposed to various flaws and damage during the manufacturing, assembly, or ground handling phase. In regard to the engine, its performance characteristics can be significantly affected by the degradation resulting from such flaws and damages, which, in turn, might lead to failure of the entire space mission. Any manufacturing/operational damage needs to be detected at the earliest stage possible, so that the required preventive measures can be implemented, and component readiness and reliability must be checked either during manufacturing or during field inspections. This review study lists such possible flaws/damages on rocket engine components. This information could be beneficial for determining and developing the efficient techniques for reliable nondestructive evaluation and structural health monitoring.
    Journal of Intelligent Material Systems and Structures 02/2013; 25(5):524-540. DOI:10.1177/1045389X13493360 · 2.17 Impact Factor
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    ABSTRACT: In 1420 Brunelleschi began construction on the enormous dome of Santa Maria del Fiore church in Florence Italy [1]. Of special interest was the fact that his design did not require temporary supports from below(the distance from the ground level to the dome was too high to build such scaffolding/centering). Instead he made use of the stabilizing effect of compressive hoop stresses. As the dome went up, it was always in balance since no part extended beyond the other. Thus, the compressive forces were distributed both radially ...
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    ABSTRACT: As the demand for wind energy increases, industry and policymakers have been pushing to place larger wind turbines in denser wind farms. Furthermore, there are higher expectations for reliability of turbines, which require a better understanding of the complex interaction between wind turbines and the fluid flow that drives them. As a test platform, we used the Whisper 500 residential scale wind turbine to support structural and atmospheric modeling efforts undertaken to improve understanding of these interactions. The wind turbine’s flexible components (blades, tower, etc.) were modeled using finite elements, and modal tests of these components were conducted to provide data for experimental validation of the computational models. Finally, experimental data were collected from the wind turbine under real-world operating conditions. The FAST (Fatigue, Aerodynamics, Structures, and Turbulence) software developed at the National Renewable Energy Laboratory was used to predict total system performance in terms of wind input to power output along with other experimentally measurable parameters such as blade tip and tower top accelerations. This paper summarizes the laboratory and field test experiments and concludes with a discussion of the models’ predictive capability. LA-UR-12-24832.
    01/2013: pages 521-533;
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    ABSTRACT: For the last 20 years the goal of the structural health monitoring community has been to endow man-made structures with a biologically-inspired nervous system in order to detect, localize, and quantify damage in structures. The effort has focused on collecting a wide array of measurements from sensor networks, extracting features from the data, comparing the data to models, and trying to use this information to determine the presence, extent and type of damage. Typically the Structural Health Monitoring community tries to make predictions of the remaining service life of the structure. It is generally assumed that there will be as little human intervention in this process as possible unless a high-consequence decision must be made. A number of advances have been made in structural health monitoring using this approach over the course of the last decade, but we are still struggling to build autonomous machines that can match the ability of a human to detect, localize and quantify damage in structures. This work aims to explore a new paradigm - cooperative human-machine structural health monitoring. The premise of this paradigm is the idea that a human cooperating with a machine will always significantly outperform a machine or human acting independently. There is no reason to not make full use of human resources that are available to us today. Furthermore, the regulatory and litigious environments that exist today for safety-critical structures are going to make it difficult to adopt health monitoring systems that effectively eliminate humans. Why not instead enhance the natural sensing and perception of human inspectors? During the course of this research effort a vibro-tactile haptic interface is under development that will in some sense allow a human to “feel” the pain of a structure when it is damaged. A number of different studies from the neuroscience community [1], [2], have indicated that it is possible to use “sensory substitution- 201D; to provide some restoration for lost senses such as sight. In this work we consider the possibility of extending the introception of a human to an external structure. This type of capability will help enable a wide variety of cyber-physical systems that must maintain reliability as well as interact with humans. For instance it may be possible to outfit a single human inspector with a haptic interface so they can single-handedly monitor a whole wind farm as if it were a natural extension of their own body. Alternatively, a single person with a haptic interface may be able to sense the state-of-health of a large ocean linear or an entire swarm of flying robots. These ideas will lead to creating a new class of high-performance, cyber-physical systems.
    RO-MAN, 2013 IEEE; 01/2013
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    ABSTRACT: As structural health monitoring continues to gain popularity, both as an area of research and as a tool for use in industrial applications, the number of technologies associated with structural health monitoring will also continue to grow. As a result, the engineer tasked with developing a structural health monitoring system is faced with myriad hardware and software technologies from which to choose, often adopting an ad hoc qualitative approach based on physical intuition or past experience to making such decisions, and offering little in the way of justification for a particular decision. This article offers a framework that aims to provide the engineer with a quantitative approach for choosing from among a suite of candidate structural health monitoring technologies. The framework is outlined for the general case, where a supervised learning approach to structural health monitoring is adopted and is then demonstrated on two problems commonly encountered when developing structural health monitoring systems: (a) selection of damage-sensitive features, where the engineer must determine the appropriate order of an autoregressive model for modeling of time-history data, and (b) selection of a damage classifier, where the engineer must select from among a suite of candidate classifiers, the one most appropriate for the task at hand. The data employed for these problems are taken from a preliminary study that examined the feasibility of applying structural health monitoring technologies to the RAPid Telescopes for Optical Response observatory network.
    Structural Health Monitoring 11/2012; 11(6). DOI:10.1177/1475921712451956 · 3.21 Impact Factor
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    ABSTRACT: This paper is a report of an investigation into tracking and monitoring the integrity of bolted joints using piezoelectric active sensors. The target application of this study is a fitting lug assembly of unmanned aerial vehicles (UAVs), where a composite wing is mounted to a UAV fuselage. The structural health monitoring methods deployed in this study are time‐series analysis and high‐frequency response functions measured by piezoelectric active sensors. Different types of simulated damage are introduced into the structure, and the capability of each technique is examined. Practical implementation issues, including temperature changes, are also considered in this study. The results collected from the tests show that piezoelectric active sensors and associated signal processing tools can be efficiently used for identifying joint failure modes of a lug assembly. Copyright © 2012 John Wiley & Sons, Ltd.
    Structural Control and Health Monitoring 11/2012; 19(7). DOI:10.1002/stc.1507 · 1.73 Impact Factor

Publication Stats

8k Citations
199.17 Total Impact Points

Institutions

  • 2–2015
    • Los Alamos National Laboratory
      • Space Science and Applications Group
      Los Alamos, California, United States
  • 2012
    • New Mexico Institute of Mining and Technology
      • Department of Mechanical Engineering
      Socorro, NM, United States
    • Chonnam National University
      Gwangju, Gwangju, South Korea
    • Sapienza University of Rome
      Roma, Latium, Italy
  • 2010
    • University of Bristol
      • Department of Aerospace Engineering
      Bristol, England, United Kingdom
    • SAIC
      San Diego, California, United States
  • 2007–2010
    • Virginia Polytechnic Institute and State University
      • Department of Mechanical Engineering
      Blacksburg, VA, United States
    • Georgia Institute of Technology
      • School of Civil & Environmental Engineering
      Atlanta, Georgia, United States
  • 2002–2009
    • The University of Sheffield
      • Department of Mechanical Engineering
      Sheffield, ENG, United Kingdom
    • Purdue University
      • School of Mechanical Engineering
      ウェストラファイエット, Indiana, United States
  • 2006–2007
    • University of California, San Diego
      San Diego, California, United States
  • 2004
    • University of Michigan
      • Department of Civil and Environmental Engineering
      Ann Arbor, Michigan, United States