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.

Wireless energy transmission to supplement energy harvesters in sensor network applications

Abstract: In this paper we present a method for coupling wireless energy transmission with traditional energy harvesting techniques in order to power sensor nodes for structural health monitoring applications. The goal of this study is to develop a system that can be permanently embedded within civil structures without the need for on-board power sources. Wireless energy transmission is included to supplement energy harvesting techniques that rely on ambient or environmental, energy sources. This approach combines several transducer types that harvest ambient energy with wireless transmission sources, providing a robust solution that does not rely on a single energy source. Experimental results from laboratory and field experiments are presented to address duty cycle limitations of conventional energy harvesting techniques, and the advantages gained by incorporating a wireless energy transmission subsystem. Methods of increasing the efficiency, energy storage medium, target applications and the integrated use of energy harvesting sources with wireless energy transmission will be discussed.

Damage detection using frequency domain ARX models and extreme value statistics

Abstract: Structural health monitoring (SHM) is fast becoming a field of great importance as engineers seek for new ways to ensure the safety of structures throughout their designed lifetime. Current methods for analyzing the dynamic response of structures often use standard frequency response functions to model linear system input/output relationships. However, these functions do not account for the nonlinear response of a system, which damage often introduces. In this study, an auto-regressive model with exogenous inputs (ARX) in the frequency domain is used to extract damage sensitive features, explicitly considering the nonlinear effect in the frequency domain. Furthermore, because of the non-Gaussian nature of the extracted features, extreme value statistics (EVS) is employed to develop a robust damage classifier. The applicability of the ARX model combined with EVS to nonlinear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model.

Full-field Structural Dynamics by Video Motion Manipulation

Abstract: Structures with complex geometries, material properties, and boundary conditions, exhibit spatially local, temporally transient, dynamic behaviors. High spatial and temporal resolution vibration measurements and modeling are thus required for high-fidelity characterization, analysis, and prediction of the structure’s dynamic phenomena. For example, high spatial resolution mode shapes are needed for accurate vibration-based damage localization. Also, higher order vibration modes typically contain local structural features that are essential for highfidelity dynamic modeling of the structure. In addition, while it is possible to build a highlyrefined mathematical model (e.g., a finite element model) of the structure, it needs to be experimentally validated and updated with high-resolution vibration measurements. However, it is a significant challenge to obtain high-resolution vibration measurements using traditional techniques. For example, accelerometers and strain-gauge sensors provide low spatial resolution measurements. Laser vibrometers provide high-resolution measurements, but are expensive and make sequential measurements that are time-consuming. On the other hand, digital video cameras are relatively low-cost, agile, and provide high spatial resolution, simultaneous, measurements. A new framework is first developed for the blind extraction and visualization of the full-field, highresolution, dynamic parameters of an operating (output-only) structure from the digital video measurements using video motion manipulation and unsupervised machine learning techniques. See Fig. 1 for the experimental results of a vibrating cantilever beam and more video demos at http://www.lanl.gov/projects/national-security-educationcenter/engineering/research-projects/blind-modal-id.php.

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.