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.

Crack detection sensor layout and bus configuration analysis

Abstract: In crack detection applications large sensor arrays are needed to be able to detect and locate cracks in structures. Emerging graphene-oxide paper sensing skins are a promising technology that will help enable structural sensing skins, but in order to make use of them we must consider how the sensors will be laid out and wired on the skin. This paper analyzes different sensor shapes and layouts to determine the layout which provides the preferred performance. A ‘snaked hexagon’ layout is proposed as the preferred sensor layout when both crack detection and crack location parameters are considered. In previous work we have developed a crack detection circuit which reduces the number of channels of the system by placing several sensors onto a common bus line. This helps reduce data and power consumption requirements but reduces the robustness of the system by creating the possibility of losing sensing in several sensors in the event that a single wire breaks. In this paper, sensor bus configurations are analyzed to increase the robustness of the bused sensor system. Results show that spacing out sensors in the same bus as much as possible increases the robustness of the system and that at least 3 buses are needed to prevent large segments of a structure from losing sensing in the event of a bus failure. This work is a preliminary effort toward enabling a new class of ‘networked materials’ that will be vitally important for next generation structural applications. ‘Networked materials’ have material properties related to information theoretic concepts. An example material property is ‘bandwidth’ per unit of material that might indicate the amount of information the material can provide about its state-of-health.

Monitoring of bolted joints using piezoelectric active-sensing for aerospace applications

Abstract: This paper is a report of an initial 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 SHM methods deployed in this study are impedance-based SHM techniques, 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 and compared. Additional considerations encountered in this initial investigation are made to guide further thorough research required for the successful field deployment of this technology.

Damage Identification Using Impedance Methods Coupled With Statistical Classifiers

Abstract: This paper presents the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems. In particular, we propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications. The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of damage identification applications. Relying on high frequency local excitations (typically>30 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. In this study, in order to diagnosis damage with levels of statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified autoregressive model with exogenous inputs (ARX) in the frequency domain is developed. The damage sensitive feature is then computed by differentiating the measured impedance and the output of the ARX model. Furthermore, because of the non-Gaussian nature of the feature distribution tails, extreme value statistics (EVS) are employed to develop a robust damage classifier. By incorporating EVS, we establish a rigorous impedance-based health monitoring algorithm, which is able to provide structural systems with self-contained and selfdiagnostic components. This paper concludes with a numerical example on a 5 degree-of-freedom system and an experimental investigation on a multi-story building model to demonstrate the performance of the proposed concept.

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.