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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 Jun 2000
TL;DR: A novel time series analysis procedure is presented to localize damage sources in a mechanical system by solely analysing the vibration signatures recorded from a structure of interests using an eight degrees-of-freedom (DOF) mass-spring system.
Abstract: A novel time series analysis procedure is presented to localize damage sources in a mechanical system An attempt is made to pinpoint the sources of nonlinear damage by solely analysing the vibration signatures recorded from a structure of interests First, a linear prediction model, combining Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) techniques, is estimated using a time series recorded under an undamaged stage of the structure Then, the residual error, which is the difference between the actual time measurement and the prediction from the previously estimated AR-ARX combined model, is defined as our damage-sensitive feature This study is based on the premise that if there were damage in the structure, the prediction model previously identified using the undamaged time history data would not be able to reproduce the newly obtained time series data measured under a damaged state of the structure Furthermore, the increase of the residual errors would be maximised at the sensors instrumented near the actual damage locations The applicability of this approach is demonstrated using the vibration test data obtained from an eight degrees-of-freedom (DOF) mass-spring system 1 505-667-6135 (Voice), 505-665-7836 (Fax), sohn@lanlgov (E-mail) 2 505-667-4551 (Voice), 505-665-2137 (Fax), farrar@lanlgov (E-mail)

7 citations

Reference EntryDOI
15 Sep 2009
TL;DR: In this article, the authors present an overview and recent applications in impedance-based structural health monitoring (SHM), and demonstrate how this technique can be efficiently used to detect structural damage in real time.
Abstract: This article presents an overview and recent applications in impedance-based structural health monitoring (SHM). The basic principle behind this technique is to apply high-frequency structural excitations (typically greater than 30 kHz) through surface-bonded piezoelectric transducers, and measure the local mechanical impedance by monitoring the current and voltage applied to the piezoelectric transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred. Previous experimental studies are summarized to demonstrate how this technique can be efficiently used to detect structural damage in real time. The impedance method also has applications in the field of sensor self-diagnostics and validation in determining the operational status of piezoelectric active sensors used in SHM. The feasibility of this sensor validation procedure is demonstrated by analytical studies and experimental examples, where the functionality of surface-mounted piezoelectric sensors was continuously deteriorated. Keywords: damage detection; piezoelectric transducers; sensor validation; impedance method

7 citations

01 Jan 2000
TL;DR: In this article, a comparison is made between a linear discriminant classifier and a general Bayesian classifier for the purpose of determining the existence of damage in a laboratory test structure.
Abstract: Many aerospace, civil, and mechanical systems continue to be used despite aging and the associated potential for damage accumulation. Therefore, the ability to monitor the structural health of these systems is becoming increasingly important. A wide variety of highly effective local non-destructive evaluation tools are available. However, damage identification based upon changes in vibration characteristics is one of the few methods that monitor changes in the structure on a global basis. The process of vibration-based damage detection will be described as a problem in statistical pattern recognition. This process is composed of four portions: 1.) Operational Evaluation, 2.)Data acquisition and cleansing; 3.) Feature selection and data compression, and 4.) Statistical model development. Current studies regarding supervised learning methods for statistical model development are discussed and emphasized with the application of this technology to a laboratory test structure. Specifically, a comparison is made between a linear discriminant classifier and a general Bayesian classifier for the purpose of determining the existence of damage.

7 citations

Proceedings ArticleDOI
10 Apr 2007
TL;DR: In this article, a PZT sensor diagnostic and validation procedure that performs in-situ monitoring of structural health monitoring (SHM) applications is presented, where the authors quantify and classify several key characteristics of temperature change and develop effcient signal processing techniques to account for those variations in the sensors diagnosis process.
Abstract: This paper presents a piezoelectric sensor diagnostic and validation procedure that performs in-situ monitoring of the operational status of piezoelectric (PZT) sensor/actuator arrays used in structural health monitoring (SHM) applications The validation of the proper function of a sensor/actuator array during operation, is a critical component to a complete and robust SHM system, especially with the large number of active sensors typically involved The method of this technique used to obtain the health of the PZT transducers is to track their capacitive value, this value manifests in the imaginary part of measured electrical admittance Degradation of the mechanical/electrical properties of a PZT sensor/actuator as well as bonding defects between a PZT patch and a host structure can be identified with the proposed procedure However, it was found that temperature variations and changes in sensor boundary conditions manifest themselves in similar ways in the measured electrical admittances Therefore, we examined the effects of temperature variation and sensor boundary conditions on the sensor diagnostic process The objective of this study is to quantify and classify several key characteristics of temperature change and to develop effcient signal processing techniques to account for those variations in the sensor diagnosis process In addition, we developed hardware capable of making the necessary measurements to perform the sensor diagnostics and to make impedance-based SHM measurements The paper concludes with experimental results to demonstrate the effectiveness of the proposed technique

7 citations

Journal ArticleDOI
TL;DR: Current research regarding feature selection and statistical model development will be emphasized with the application of this technology to large‐scale, in situ bridge structures and to bridge columns that were tested...
Abstract: The ability to monitor the structural health of our aging infrastructure is becoming increasingly important A wide variety of highly effective local nondestructive evaluation tools are available However, damage identification based upon changes in vibration characteristics is one of the few methods that monitors changes in the structure on a global basis The material presented herein will summarize the structural health monitoring research that has been conducted at Los Alamos National Laboratory over the last 8 years First, the process of vibration‐based damage detection will be described as a problem in statistical pattern recognition This process has three portions: (1) data acquisition and cleansing; (2) feature selection and data compression; and (3) statistical model development Current research regarding feature selection and statistical model development will be emphasized with the application of this technology to large‐scale, in situ bridge structures and to bridge columns that were tested

7 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