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Charles R. Farrar

Other affiliations: Analysis Group
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.


Papers
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01 Jan 2009
TL;DR: A multi-tiered approach for sensing and data processing will be discussed as potential SHM architecture for future shipboard application, and a recent wireless hull monitoring demo on FSF-I SeaFighter will be shown as an example to show how this proposed architecture is a viable approach for long-term and real-time hull monitoring.
Abstract: A comprehensive structural health monitoring (SHM) system is a critical mechanism to ensure hull integrity and evaluate structural performance over the life of a ship, especially for lightweight high-speed ships. One of the most important functions of a SHM system is to provide real-time performance guidance and reduce the risk of structural damage during operations at sea. This is done by continuous feedback from onboard sensors providing measurements of seaway loads and structural responses. Applications of SHM should also include diagnostic capabilities such as identifying the presence of damage, assessing the location and extent of damage when it does occur in order to plan for future inspection and maintenance. The development of such SHM systems is extremely challenging because of the physical size of these structures, the widely varying and often extreme operational and environmental conditions associated with the missions of high performance ships, the lack of data from known damage conditions, the limited sensing that was not designed specifically for SHM, the management of the vast amounts of data, and the need for continued, real-time data processing. This paper will discuss some of these challenges and several outstanding issues that need to be addressed in the context of applyingmore » various SHM approaches to sea trials data measured on an aluminum high-speed catamaran, the HSV-2 Swift. A multi-tiered approach for sensing and data processing will be discussed as potential SHM architecture for future shipboard application. This approach will involve application of low cost and dense sensor arrays such as wireless communications in selected areas of the ship hull in addition to conventional sensors measuring global structural response of the ship. A recent wireless hull monitoring demo on FSF-I SeaFighter will be discussed as an example to show how this proposed architecture is a viable approach for long-term and real-time hull monitoring.« less

2 citations

Journal Article
TL;DR: In this paper, the authors present the SHM result of a 9m CX-100 wind turbine blade under full-scale fatigue loads at the National Renewable Energy Laboratory (NERL).
Abstract: : This paper presents the SHM result of a 9m CX-100 wind turbine blade under full-scale fatigue loads. The test was performed at the National Renewable Energy Laboratory. The 9-meter blade was instrumented with piezoelectric transducers, accelerometers, acoustic emission sensors, and foil strain gauges on the surface of the blade. The blade underwent fatigue excitation at 1.8 Hz for defined intervals, and data from the sensors were collected between and during fatigue loading sessions. The data were measured at multi-scale, high frequency ranges for identifying fatigue damage initiation, and low-frequency ranges for assessing damage progression. High and Low frequency response functions, time series based methods, and Lamb wave date measured by piezoelectric transducers were utilized to analyze the condition of the turbine blade, along with other sensing systems (acoustic emission). A specially designed hardware developed by Los Alamos National Laboratory was also implemented for performance comparison. This paper summarizes considerations needed to design such SHM systems, experimental procedures and results, and additional issues that can be used as guidelines for future investigations.

2 citations

Book ChapterDOI
01 Jan 2021
TL;DR: Probabilistic risk assessment (PRA) is an established methodology for quantifying risks, used by engineers in a range of industries to inform decisions regarding the design and operation of safety-critical or high-value structures and systems as discussed by the authors.
Abstract: The notion of risk is comprised of two components: the likelihood of an adverse event occurring, and the severity of the consequences. Probabilistic risk assessment (PRA) is an established methodology for quantifying risks, used by engineers in a range of industries to inform decisions regarding the design and operation of safety-critical or high-value structures and systems.

2 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

Journal ArticleDOI
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
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.

9,627 citations

Journal ArticleDOI
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.

3,848 citations

ReportDOI
01 May 1996
TL;DR: 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 is presented in this article, where the authors categorize the methods according to required measured data and analysis technique.
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.

2,916 citations