Charles R. Farrar

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

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Publications (282)103.36 Total impact

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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. · 2.02 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 01/2014; 333(6):1833–1850. · 1.61 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). · 1.44 Impact Factor
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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: Increasing involvement of commercial enterprises in space activities is among leading forces behind a renewed interest in structural diagnostic methodologies promising potential for improving safety, operability and cost effectiveness of launch vehicles and spaceships. It is envisioned that unobtrusive, real time structural health monitoring (SHM) systems may assist in space vehicle's prelaunch qualification, orbital operation, safe disintegration during reentry or recertification for a next flight. SHM experiment utilizing piezoelectric wafer active sensors in conjunction with electro-mechanical impedance measurements has been develop to explore feasibility of active structural health monitoring during suborbital space flight. Details of experiment are discussed and some results obtained in real time for all segments of vehicle's trajectory are presented. Experimental data collected during suborbital space flight has shown feasibility of SHM in the challenging environment, utility of thin wafer piezoelectric sensors as active elements of spacecraft SHM system, and potential of the electro-mechanical impedance method for real time structural integrity assessment of the payload.
    The Journal of the Acoustical Society of America 09/2012; 132(3):1964. · 1.65 Impact Factor
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    ABSTRACT: The aim of the paper is to study the possibility of implementing modal filtering techniques for damage detection in the presence of non-linearities in the recorded signals. Initially designed for linear damage detection the method is based on the linear combination of the sensors responses, a transformation to the frequency domain, and the computation of peak indicators which are used subsequently in an outlier analysis process. The efficiency of the method to detect both linear and nonlinear damage scenarios is assessed using data recorded on the three-storey frame structure previously developed and studied at Los Alamos National Labs. Experimental data consists in four acceleration records. Besides the baseline condition, both linear (mass and stiffness changes) and non-linear (bumper device) changes have been considered. The results obtained using the modal filtering approach are compared to the ones obtained based on auto-regressive models, considering either the auto-regressive parameters or the time-domain residuals.
    6th European Workshop on Structural Health Monitoring, Dresden, Germany; 07/2012
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    ABSTRACT: Mobile sensor nodes are an ideal solution for efficiently collecting measurements for a variety of applications. Mobile sensor nodes offer a particular advantage when measurements must be made in hazardous and/or adversarial environments. When mobile sensor nodes must operate in hostile environments, it would be advantageous for them to be able to avoid undesired interactions with hostile elements. It is also of interest for the mobile sensor node to maintain low-observability in order to avoid detection by hostile elements. Conventional path-planning strategies typically attempt to plan a path by optimizing some performance metric. The problem with this approach in an adversarial environment is that it may be relatively simple for a hostile element to anticipate the mobile sensor node's actions (i.e. optimal paths are also often predictable paths). Such information could then be leveraged to exploit the mobile sensor node. Furthermore, dynamic adversarial environments are typically characterized by high-uncertainty and highcomplexity that can make synthesizing paths featuring adequate performance very difficult. The goal of this work is to develop a path-planner anchored in info-gap decision theory, capable of generating non-deterministic paths that satisfy predetermined performance requirements in the face of uncertainty surrounding the actions of the hostile element(s) and/or the environment. This type of path-planner will inherently make use of the time-tested security technique of varying paths and changing routines while taking into account the current state estimate of the environment and the uncertainties associated with it.
    Proc SPIE 05/2012;
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    ABSTRACT: The paper presents a discussion of the design, development, and assembly of Structural Health Monitoring (SHM) experiments launched in space on a sub-orbital flight. Onboard experiments were focused on investigating the utility of piezoelectric wafer active sensors (PWAS) as active elements of spacecraft SHM systems and the electro-mechanical impedance method as a promising SHM methodology for space systems. A Magneto-elastic active sensor (MEAS) was used to record in-flight dynamics of the payload. The list of PWAS experiments included a bolted-joint experiment, an adhesive endurance experiment, and an experiment to monitor PWAS condition during spaceflight. Electromechanical impedances of piezoelectric sensors were recorded in-flight at varying input frequencies using onboard microcontroller units. PWAS and MEAS data were recovered from the payload after landing. Details of the sub-orbital flight experiments are considered and conclusions pertaining to flight results are presented. The paper discusses issues encountered during design, development, and assembly of the payload and aspects central to successful demonstration of the SHM during sub-orbital space flight.
    Proc SPIE 03/2012;
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    ABSTRACT: The acoustic emission (AE) phenomena generated by a rapid release in the internal stress of a material represent a promising technique for structural health monitoring (SHM) applications. AE events typically result in a discrete number of short-time, transient signals. The challenge associated with capturing these events using classical techniques is that very high sampling rates must be used over extended periods of time. The result is that a very large amount of data is collected to capture a phenomenon that rarely occurs. Furthermore, the high energy consumption associated with the required high sampling rates makes the implementation of high-endurance, low-power, embedded AE sensor nodes difficult to achieve. The relatively rare occurrence of AE events over long time scales implies that these measurements are inherently sparse in the spike domain. The sparse nature of AE measurements makes them an attractive candidate for the application of compressed sampling techniques. Collecting compressed measurements of sparse AE signals will relax the requirements on the sampling rate and memory demands. The focus of this work is to investigate the suitability of compressed sensing techniques for AE-based SHM. The work explores estimating AE signal statistics in the compressed domain for low-power classification applications. In the event compressed classification finds an event of interest, ι1 norm minimization will be used to reconstruct the measurement for further analysis. The impact of structured noise on compressive measurements is specifically addressed. The suitability of a particular algorithm, called Justice Pursuit, to increase robustness to a small amount of arbitrary measurement corruption is investigated.
    Proc SPIE 03/2012;
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    ABSTRACT: This paper presents the deployment of an embedded active sensing platform for real-time condition monitoring of telescopes in the RAPid Telescopes for Optical Response (RAPTOR) observatory network. The RAPTOR network consists of several ground-based autonomous astronomical observatories primarily designed to search for astrophysical transients such as gamma-ray bursts. In order to capture astrophysical transients of interest, the telescopes must remain in peak operating condition to move swiftly from one potential transient to the next throughout the night. However, certain components of these telescopes have until recently been maintained in an ad hoc manner, often being permitted to run to failure, resulting in the inability to drive the telescope. In a recent study, a damage classifier was developed using the statistical pattern recognition paradigm of structural health monitoring (SHM) to identify the onset of damage in critical telescope drive components. In this work, a prototype embedded active sensing platform is deployed to the telescope structure in order to record data for use in detecting the onset of telescope drive component damage and alert system administrators prior to system failure.
    Proc SPIE 03/2012;
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    ABSTRACT: The RAPid Telescopes for Optical Response (RAPTOR) observatory network consists of several robotic astronomical telescopes primarily designed to search for astrophysical transients called a gamma-ray bursts (GRBs). Although intrinsically bright, GRBs are difficult to detect because of their short duration. Typically, they are first observed by satellites that then relay the coordinates of the GRB to a ground station which, in turn, distributes the coordinates over the internet so that ground based observers can perform follow-up observations. Typically the ground based observations begin after the GRB has ended and only residual emiSSion (the 'afterglow') is left. However, if the satellite relays the GRB coordinates quickly enough, a 'fast' robotic telescope on the ground may be able to catch the GRB in progress. The RAPTOR telescope system is one of only a few in the world to have accomplished this feat. In order to achieve these results, the RAPTOR telescopes must operate autonomously at a high duty-cycle and in peak operating condition. Currently the telescopes are maintained in an ad hoc manner, often in a run-to-failure mode. The RAPTOR project could benefit greatly from a structural health monitoring (SHM) system, especially as more complex units are added to the suite of telescopes. This paper will summarize preliminary results from an SHM study performed on one of the RAPTOR telescopes. Damage scenarios that are of concern and that have been previously observed are first summarized. Then a specific study of damage to the telescope drive mechanism is presented where the data acquisition system is first described. Next, damage detection algorithms are developed with LANL's new publically available software SHMTools and the results of this process are discussed in detail. The paper will conclude with a summary of future planned refinemenls of the RAPTOR SHM system.
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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 01/2012; 19(7). · 1.54 Impact Factor
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    ABSTRACT: The remarkable evolution of new generation wind turbines has led to a dramatic increase of wind turbine blade size. In turn, a reliable structural health monitoring (SHM) system will be a key factor for the successful implementation of such systems. Detection of damage at an early stage is a crucial 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 Frequency Response Functions (FRFs) extracted by using an input/output acquisition technique under a fatigue loading of a 9m CX-100 blade at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC) performed in the Los Alamos National Laboratory. The blade was harmonically excited at its first natural frequency using a Universal Resonant Excitation (UREX) system. For analysis, the Auto-Associative Neural Network (AANN) is a non-parametric method where a set of damage sensitive features gathered from the measured structure are used to train a network that acts as a novelty detector. This traditionally has a highly complex "bottleneck" structure with five layers in the AANN. In the current paper, a new attempt is also exploited based on an AANN with one hidden layer in order to reduce the theoretical and computational difficulties. Damage detection of composite bodies of blades is a "grand challenge" due to varying aerodynamic and gravitational loads and environmental conditions. A study of the noise tolerant capability of the AANN which is associated to its generalisation capacity is addressed. It will be shown that vibration response data combined with AANNs is a robust and powerful tool, offering novelty detection even when operational and environmental variations are present. The AANN is a method which has not yet been widely used in the structural health monitoring of composite blades.
    Journal of Physics Conference Series 01/2012; 382(1).
  • 01/2012;
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    ABSTRACT: Mobile sensor nodes hold great potential for collecting field data using fewer resources than human operators would require and potentially requiring fewer sensors than a fixed-position sensor array. It would be very beneficial to allow these mobile sensor nodes to operate unattended with a minimum of human intervention. In order to allow mobile sensor nodes to operate unattended in a field environment, it is imperative that they be capable of identifying and responding to external agents that may attempt to tamper with, damage or steal the mobile sensor nodes, while still performing their data collection mission. Potentially hostile external agents could include animals, other mobile sensor nodes, or humans. This work will focus on developing control policies to help enable a mobile sensor node to identify and avoid capture by a hostile un-mounted human. The work is developed in a simulation environment, and demonstrated using a non-holonomic, ground-based mobile sensor node. This work will be a preliminary step toward ensuring the cyber-physical security of ground-based mobile sensor nodes that operate unattended in potentially unfriendly environments.
    Proc SPIE 10/2011;
  • C. R. Farrar, G. Park, M. D. Todd
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    ABSTRACT: The process of structural health monitoring (SHM) requires an integrated paradigm of networked sensing and actuation, data interrogation (signal processing and feature extraction), and statistical assessment (classification of damage existence, location, and/or type) that treats structural health assessments in a systematic way. An appropriate sensor network is always required in observing the structural system behaviour in such a way that suitable signal processing and damage-sensitive feature extraction on the measured data can be performed efficiently. Consequently, several sensing network paradigms for SHM have emerged in the past, and this chapter is intended to provide an overview of these paradigms. Various parameters of SHM sensing systems that must be considered in its design and subsequent field deployment are also summarized.
    07/2011: pages 137-157;
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    ABSTRACT: In order to realize the wide-scale deployment of high-endurance, unattended mobile sensing technologies, it is vital to ensure the self-preservation of the sensing assets. Deployed mobile sensor nodes face a variety of physical security threats including theft, vandalism and physical damage. Unattended mobile sensor nodes must be able to respond to these threats with control policies that facilitate escape and evasion to a low-risk state. In this work the Precision Immobilization Technique (PIT) problem has been considered. The PIT maneuver is a technique that a pursuing, car-like vehicle can use to force a fleeing vehicle to abruptly turn ninety degrees to the direction of travel. The abrupt change in direction generally causes the fleeing driver to lose control and stop. The PIT maneuver was originally developed by law enforcement to end vehicular pursuits in a manner that minimizes damage to the persons and property involved. It is easy to imagine that unattended autonomous convoys could be targets of this type of action by adversarial agents. This effort focused on developing control policies unattended mobile sensor nodes could employ to escape, evade and recover from PIT-maneuver-like attacks. The development of these control policies involved both simulation as well as small-scale experimental testing. The goal of this work is to be a step toward ensuring the physical security of unattended sensor node assets.
    Proc SPIE 05/2011;
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    ABSTRACT: This study focuses on defining and comparing response features that can be used for structural dynamics model validation studies. Features extracted from dynamic responses obtained analytically or experimentally, such as basic signal statistics, frequency spectra, and estimated timeseries models, can be used to compare characteristics of structural system dynamics. By comparing those response features extracted from experimental data and numerical outputs, validation and uncertainty quantification of numerical model containing uncertain parameters can be realized. In this study, the applicability of some response features to model validation is first discussed using measured data from a simple test-bed structure and the associated numerical simulations of these experiments. Issues that must be considered were sensitivity, dimensionality, type of response, and presence or absence of measurement noise in the response. Furthermore, we illustrate a comparison method of multivariate feature vectors for statistical model validation. Results show that the outlier detection technique using the Mahalanobis distance metric can be used as an effective and quantifiable technique for selecting appropriate model parameters. However, in this process, one must not only consider the sensitivity of the features being used, but also correlation of the parameters being compared.
    03/2011: pages 153-163;
  • Kevin M. Farinholt, Stuart G. Taylor, Charles R. Farrar
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    ABSTRACT: This paper presents the performance of a variety of structural health monitoring (SHM) techniques, based on the use of piezoelectric active sensors, to determine the structural integrity of a 9m CX-100 wind turbine blade (developed by Sandia National Laboratory). First, the dynamic characterization of a CX-100 blade is performed using piezoelectric transducers, where the results are compared to those by conventional accelerometers. Several SHM techniques, including Lamb wave propagations, frequency response functions, and time series based methods are then utilized to analyze the condition of the wind turbine blade. The main focus of this research is to assess and construct a performance matrix to compare the performance of each method in identifying incipient damage, with a special consideration given the issues related to field deployment. Experiments are conducted on a stationary, full length CX-100 wind turbine blade. This examination is a precursor for planned full-scale fatigue testing of the blade and subsequent tests to be performed on an operational CX-100 Rotor Blade to be flown in the field.
    Proc SPIE 03/2011;
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    ABSTRACT: Wireless sensor nodes with impedance measurement capabilities, often based on the Analog Devices AD5933 impedance chip and Atmel's 8-bit ATMega 1281 microcontroller, have been demonstrated to be effective in collecting data for localized damage detection (such as for loose bolt detection) and for sensor self-diagnostics. Previouslydeveloped nodes rely on radio telemetry and off-board processing (usually via a PC) to ascertain damage presence or sensor condition. Recent firmware improvements for the wireless impedance device (WID) now allow seamless integration of the WID with SHMTools and mFUSE, an open-source function sequencer and SHM process platform for Matlab. Furthermore, SHM processes developed using mFUSE can be implemented in hardware on the WID, allowing greater autonomy among the sensor nodes to identify and report damage in real time. This paper presents the capabilities of the newly integrated hardware and software, as well as experimental validation.
    Proc SPIE 03/2011;

Publication Stats

4k Citations
103.36 Total Impact Points


  • 2–2014
    • 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
  • 2010
    • Virginia Polytechnic Institute and State University
      • Department of Mechanical Engineering
      Blacksburg, VA, United States
  • 2–2010
    • The University of Sheffield
      • Department of Mechanical Engineering
      Sheffield, England, United Kingdom
  • 2008
    • University of California, San Diego
      • Department of Structural Engineering
      San Diego, CA, United States
  • 2007
    • Carnegie Mellon University
      • Department of Civil and Environmental Engineering
      Pittsburgh, PA, United States
  • 2002
    • Stanford University
      • Department of Mechanical Engineering
      Stanford, CA, United States
  • 2000
    • Texas Tech University
      Lubbock, Texas, United States