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 Using Modular Wireless Sensors

Abstract: System integration of an online structural health monitoring module was accomplished by coupling commercially available microelectro-mechanical system sensors and a wireless telemetry unit with damage detection firmware. To showcase the capabilities of the integrated monitoring module, a bolted frame structure was constructed, and the preload in one of the bolted joints was controlled by a piezoelectric stack actuator to simulate gradual deterioration of a bolted connection. Two separate damage detection algorithms were used to classify a joint as damaged or undamaged. First, a statistical process control algorithm was used to monitor the correlation of vibration data from two accelerometers mounted across a joint. Changes in correlation were used to detect damage to the joint. For each joint, data were processed locally on a microprocessor integrated with the wireless module, and the diagnosis result was remotely transmitted to the base monitoring station. Second, a more sophisticated damage detection al...

Health Monitoring of UAV Wing Skin-to-spar Joints using Guided Waves and Macro Fiber Composite Transducers:

Abstract: This article deals with the monitoring of the composite wing skin-to-spar joint in unmanned aerial vehicles using ultrasonic guided waves The study investigates simulated wing skin-to-spar joints with two different types of bond defects, namely poorly cured adhesive and disbonded interfaces The bond-sensitive feature considered is the ultrasonic strength of transmission through the joints The dispersive wave propagation problem is studied numerically by a semi-analytical finite element method that accounts for viscoelastic damping, and experimentally by ultrasonic testing that uses highly durable, flexible macro fiber composite transducers The discrete wavelet transform is also employed to de-noise and compress the ultrasonic measurements Both numerical and experimental tests confirm that the ultrasonic strength of transmission increases across the defected bonds

Structural Health Monitoring Studies of the Alamosa Canyon and I-40 Bridges

Abstract: From 1994 to 1997 internal research grants from Los Alamos National Laboratory's Laboratory Direct Research and Development (LDRD) office were used to fund an effort aimed at studying global vibration-based damage detection methods. To support this work, several field tests of the Alamosa Canyon Bridge have been performed to study various aspects of applying vibration-based damage detection methods to a real world in situ structure. This report summarizes the data that has been collected from the various vibration tests performed on the Alamosa Canyon Bridge, analyses of these data, and the results that have been obtained. Initially, it was the investigators' intent to introduce various types of damage into this bridge and study several vibration-based damage detection methods. The feasibility of continuously monitoring such a structure for the onset of damage was also going to be studied. However, the restrictions that the damage must be relatively benign or repairable made it difficult to take the damage identification portion of the study to completion. Subsequently, this study focused on quantifying the variability in identified modal parameters caused by sources other than damage. These sources include variability in testing procedures, variability in test conditions, and environmental variability. These variabilities must be understood and their influence on identified modal properties quantified before vibration-based damage detection can be applied with unambiguous results. Quantifying the variability in the identified modal parameters led to the development of statistical analysis procedures that can be applied to the experimental modal analysis results. It is the authors' opinion that these statistical analysis procedures represent one of the major contributions of these studies to the vibration-based damage detection field. Another significant contribution that came from this portion of the study was the extension of a strain-energy-based damage detection method originally developed for structures that exhibit beam-bending response to structures that exhibit plate-like bending or bending in two directions. In addition, based on lessons learned from the Alamosa Canyon Bridge test, data from the I-40 Bridge tests have been re-analyzed using the statistical analysis procedures developed as part of this study. The application of these statistical procedures to the I-40 Bridge test results gives particular insight into how statistical analysis can be used to enhance the vibration-based damage detection process.

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.