Charles R. Farrar

Bio: Charles R. Farrar is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Structural health monitoring & Sensor node. The author has an hindex of 70, co-authored 357 publications receiving 26338 citations. Previous affiliations of Charles R. Farrar include Analysis Group.

Time series predictive models of piezoelectric active-sensing for SHM applications

Abstract: In this paper, the use of time domain data from piezoelectric active-sensing techniques is investigated for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, the use of known and repeatable inputs at high frequency ranges makes the development of SHM signal processing algorithm easier and more efficient. However, to date, most of these techniques have been based on frequency domain analyses, such as impedance-based or high-frequency response functions (FRF) -based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or wavelets analysis for damage-sensitive feature extraction. This process usually requires excessive averaging to reduce measurement noise and more computational resources, which is not ideal from both memory and power consumption standpoints. Therefore in this study, we investigate the use of autoregressive models with exogenous inputs (ARX) with the measured time series data from piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were manually imposed. The performance of this technique is compared to that of traditional autoregressive models, traditionally used in low-frequency passive sensing SHM applications, and that of FRF-based analysis, and its superior capability for SHM is demonstrated.

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.

Identification of Full-Field Dynamic Loads on Structures Using Computer Vision and Unsupervised Machine Learning

Abstract: Real-world structures, such as civil and aerospace structures, are subjected to various dynamic loads which are spatially local and distributed. Assessment of operational performance, prediction of the dynamic responses, and prognosis of the remaining service life of the structure therefore requires accurate, high-resolution measurements, and modeling of the dynamic loads. This is extremely difficult, if not impossible, with the current state of the art. First, dynamic loads on structures usually come from a wide spectrum of sources, some of which are extremely challenging to accurately measure, such as the traffic loads on a bridge. Also, it is impractical to instrument a dense array of force measurement devices on the structure due to the high cost, the effect of mass-loading, and modification of the structure’s surface. On the other hand, digital video cameras are non-contact measurement device that are relatively low-cost, agile, and able to provide high spatial resolution, simultaneous, pixel measurements. This study develops a novel method for identification of the high-resolution, full-field loads on the structure from the video of the operational structures by leveraging advanced computer vision and unsupervised learning techniques. Impact and wind loads were applied on a cable structure to experimentally validate the method. The non-contact, remote, simultaneous sensing capability of the proposed technique should enable truly high-resolution, full-field force estimation that was previously not feasible.

Development of a novel human-machine interface exploiting sensor substitution for structural health monitoring

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” 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.

Application of a Wireless Sensor Node to Health Monitoring of Operational Wind Turbine Blades

Abstract: Structural health monitoring (SHM) is a developing field of research with a variety of applications including civil structures, industrial equipment, and energy infrastructure. An SHM system requires an integrated process of sensing, data interrogation and statistical assessment. The first and most important stage of any SHM system is the sensing system, which is traditionally composed of transducers and data acquisition hardware. However, such hardware is often heavy, bulky, and difficult to install in situ. Furthermore, physical access to the structure being monitored may be limited or restricted, as is the case for rotating wind turbine blades or unmanned aerial vehicles, requiring wireless transmission of sensor readings. This study applies a previously developed compact wireless sensor node to structural health monitoring of rotating small-scale wind turbine blades. The compact sensor node collects low-frequency structural vibration measurements to estimate natural frequencies and operational deflection shapes. The sensor node also has the capability to perform high-frequency impedance measurements to detect changes in local material properties or other physical characteristics. Operational measurements were collected using the wireless sensing system for both healthy and damaged blade conditions. Damage sensitive features were extracted from the collected data, and those features were used to classify the structural condition as healthy or damaged.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Anomaly detection: A survey

Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review

Abstract: This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.