Showing papers by "Charles R. Farrar published in 2012"

Structural Health Monitoring: A Machine Learning Perspective

Abstract: This book focuses on structural health monitoring in the context of machine learning. The authors review the technical literature and include case studies. Chapters include: operational evaluation, sensing and data acquisition, introduction to probability and statistics, machine learning and statistical pattern recognition, and data prognosis.

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

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.

On Assessing the Robustness of Structural Health Monitoring Technologies

Abstract: As Structural Health Monitoring (SHM) 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 SHM will also continue to grow As a result, the engineer tasked with developing a SHM 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 The present paper offers a framework that aims to provide the engineer with a qualitative approach for choosing from among a suite of candidate SHM technologies The framework is outlined for the general case, where a supervised learning approach to SHM is adopted, and is then demonstrated on a problem commonly encountered when developing SHM systems: 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 SHM technologies to the RAPid Telescopes for Optical Response observatory network (Approved for unlimited public release on September 20, 2011, LA-UR 11-05398, Unclassified)

Assessing joint integrity of a lug assembly using piezoelectric active sensors

Abstract: SUMMARY 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.

The application of compressed sensing to long-term acoustic emission-based structural health monitoring

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

Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data

Abstract: The Ultrasonic Propagation Imaging (UPI) System is a unique, non-contact, laser-based ultrasonic excitation and measurement system developed for structural health monitoring applications. The UPI system imparts laser-induced ultrasonic excitations at user-defined locations on a structure of interest. The response of these excitations is then measured by piezoelectric transducers. By using appropriate data reconstruction techniques, a time-evolving image of the response can be generated. A representative measurement of a plate might contain 800x800 spatial data measurement locations and each measurement location might be sampled at 500 instances in time. The result is a total of 640,000 measurement locations and 320,000,000 unique measurements. This is clearly a very large set of data to collect, store in memory and process. The value of these ultrasonic response images for structural health monitoring applications makes tackling these challenges worthwhile. Recently compressed sensing has presented itself as a candidate solution for directly collecting relevant information from sparse, high-dimensional measurements. The main idea behind compressed sensing is that by directly collecting a relatively small number of coefficients it is possible to reconstruct the original measurement. The coefficients are obtained from linear combinations of (what would have been the original direct) measurements. Often compressed sensing research is simulatedmore » by generating compressed coefficients from conventionally collected measurements. The simulation approach is necessary because the direct collection of compressed coefficients often requires compressed sensing analog front-ends that are currently not commercially available. The ability of the UPI system to make measurements at user-defined locations presents a unique capability on which compressed measurement techniques may be directly applied. The application of compressed sensing techniques on this data holds the potential to reduce the number of required measurement locations, reduce the time to make measurements, reduce the memory required to store the measurements, and possibly reduce the computational burden to classify the measurements. This work considers the appropriate selection of the signal dictionary used for signal reconstruction, and performs an evaluation of compressed sensing technique's ability to reconstruct ultrasonic images using fewer measurements than would be needed using traditional Nyquist-limited data collection techniques.« less

Incipient Crack Detection in Composite Wind Turbine Blades

Abstract: This paper presents some analysis results for incipient crack detection in a 9-meter CX-100 wind turbine blade that underwent fatigue loading to failure. The blade was manufactured to standard specifications, and it underwent harmonic excitation at its first resonance using a hydraulically-actuated excitation system until reaching catastrophic failure. This work investigates the ability of an ultrasonic guided wave approach to detect incipient damage prior to the surfacing of a visible, catastrophic crack. The blade was instrumented with piezoelectric transducers, which were used in an active, pitchcatch mode with guided waves over a range of excitation frequencies. The performance results in detecting incipient crack formation in the fiberglass skin of the blade is assessed over the range of frequencies in order to determine the point at which the incipient crack became detectable. Higher excitation frequencies provide consistent results for paths along the rotor blade's carbon fiber spar cap, but performance falls off with increasing excitation frequencies for paths off of the spar cap. Lower excitation frequencies provide more consistent performance across all sensor paths.

Wind turbine blade fatigue tests: lessons learned and application to SHM system development

Abstract: This paper presents experimental results of several structural health monitoring (SHM) methods applied to a 9-meter CX-100 wind turbine blade that underwent fatigue loading. The blade was instrumented with piezoelectric transducers, accelerometers, acoustic emission sensors, and foil strain gauges. It underwent harmonic excitation at its first natural frequency using a hydraulically actuated resonant excitation system. The blade was initially excited at 25% of its design load, and then with steadily increasing loads until it failed. Various data were collected between and during fatigue loading sessions. The data were measured over multiple frequency ranges using a variety of acquisition equipment, including off-the-shelf systems and specially designed hardware developed by the authors. Modal response, diffuse wave-field transfer functions, and ultrasonic guided wave methods were applied to assess the condition of the wind turbine blade. The piezoelectric sensors themselves were also monitored using a sensor diagnostics procedure. This paper summarizes experimental procedures and results, focusing particularly on fatigue crack detection, and concludes with considerations for implementing such damage identification systems, which will be used as a guideline for future SHM system development for operating wind turbine blades.

Workshop on transitioning structural health monitoring technology to military platforms

Abstract: Interest in structural health monitoring/management is attracting lots of attention across a spectrum that ranges from sensor developers to end users. The US military, in particular, is making a concerted effort to implement condition-based maintenance as a means of reducing the life cycle costs and improving availability of various weapon platforms. Despite this effort, the majority of installed health monitoring systems are limited to rotating machinery such as engines, transmissions, and other gear boxes. The goal of this workshop was to bring together representatives from military, industry, and academia covering the spectrum from hardware developers to end users and platform managers and have them discuss issues that must be addressed as structural health monitoring systems mature to the point that managers will implement them. This article describes those discussions and highlights important issues that need to be addressed as structural health monitoring systems make the transition from laboratory ...

Design, development, and assembly of sub-orbital space flight structural health monitoring experiment

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.

On assessing the robustness of an input signal optimization algorithm for damage detection: the Info-Gap Decision Theory approach

Abstract: The Info-Gap Decision Theory (IGDT) is here adopted to assess the robust- ness of a technique aimed at identifying the optimal excitation signal within a structural health monitoring (SHM) procedure. 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 the damage increasing the difference between measurable outputs of the undamaged and damaged system. In particular, a 2 DOF mass- spring-damper system characterized by the presence of a nonlinear stiffness is considered. Uncertainty is introduced within the system under 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 system 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 ob- tained, showing which parameters are more sensitive to the presence of uncertainty and thus enabling a clear evaluation of its robustness.

Embedded sensor node deployment to monitor telescope drive system components

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.

Application of modal filters for damage detection in the presence of non-linearities

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.

Development of an info-gap-based path planner to enable nondeterministic low-observability mobile sensor nodes

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.