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.

Structural Health Monitoring of Research-Scale Wind Turbine Blades

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.

A graphite oxide (GO)-based remote readable tamper evident seal

Abstract: This paper presents a prototype of a remotely readable graphite oxide (GO) paper-based tamper evident seal. The proposed device combines the tunable electrical properties offered by reduced graphite oxide (RGO) with a compressive sampling scheme. The benefit of using RGO as a tamper evident seal material is the sensitivity of its electrical properties to the common mechanisms adopted to defeat tamper-evident seals. RGO's electrical properties vary upon local stress or cracks induced by mechanical action (e.g., produced by shimming or lifting attacks). Further, modification of the seal's electrical properties can result from the incidence of other defeat mechanisms, such as temperature changes, solvent treatment and steam application. The electrical tunability of RGO enables the engraving of a circuit on the area of the tamper evident seal intended to be exposed to malicious attacks. The operation of the tamper evident seal, as well as its remote communication functionality, is supervised by a microcontroller unit (MCU). The MCU uses the RGO-engraved circuitry to physically implement a compressive sampling acquisition procedure. The compressive sampling scheme provides the seal with self-authentication and self-state-of-health awareness capabilities. The prototype shows potential for use in low-power, embedded, remote-operation non-proliferation security related applications.

Experimental Characterization and Predictive Modeling of a Residential-Scale Wind Turbine

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.

Nondestructive inspection using continuous ultrasonic wave generation

Abstract: Methods and apparatus are disclosed for analyzing structures by applying a continuous ultrasonic excitation and measuring steady state response of the structures using laser Doppler vibrometery, or other techniques. In one example, a method comprises applying a continuous signal having one or more periodic tones to the structure, generating measurements of wave response to the signal at each of a plurality of inspection points of the structure, and, for each of the periodic tones, estimating wavenumbers for a number of the inspection points of the structure based on the wave response measurements and the frequency of the periodic tones. The estimated wavenumbers can be used to determine properties of the structure, including defects, damage, or variation in thickness.

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.