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.

Piezoelectric Active Sensor Self-Diagnostics Using Electrical Admittance Measurements

Abstract: This paper presents a piezoelectric sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric materials used for sensors and actuators in structural health monitoring (SHM) applications. The sensor/actuator self-diagnostic procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is a critical component to successfully complete the SHM process with large numbers of active sensors typically installed in a structure. The premise of this procedure is to track the changes in the capacitive value of piezoelectric materials resulting from the degradation of the mechanical/electrical properties and its attachment to a host structure, which is manifested in the imaginary part of the measured electrical admittances. This paper concludes with an experimental example to demonstrate the feasibility of the proposed procedure.

Damage Detection in Composite Plates by Using an Enhanced Time Reversal Method

Abstract: A damage detection technique, which does not rely on any past baseline signals, is proposed to assess damage in composite plates by using an enhanced time reversal method. A time reversal concept of modern acoustics has been adapted to guided-wave propagation to improve the detectability of local defects in composite structures. In particular, wavelet-based signal processing techniques have been developed to enhance the time reversibility of Lamb waves in thin composite laminates. In the enhanced time reversal method, an input signal at an excitation point can be reconstructed if a response signal measured at another point is reemitted to the original excitation point after being reversed in a time domain. This time reversibility is based on linear reciprocity of elastic waves, and it is violated when nonlinearity is caused by a defect along a direct wave path. Examining the deviation of the reconstructed signal from the known initial input signal allows instantaneous identification of damage without requiring the baseline signal for comparison. The validity of the proposed method has been exemplified through experimental studies on a quasi-isotropic laminate with delamination.

Variability of Modal Parameters Measured on the Alamosa Canyon Bridge

Abstract: A significant amount of work has been reported in technical literature regarding the use of changes in modal parameters to identify the location and extent of damage in structures. Curiously absent, and critically important to the practical implementation of this work, is an accurate characterization of the natural variability of these modal parameters caused by effects other than damage. To examine this issue, a two-lane, seven-span, composite slab-on-girder bridge near the town of Truth or Consequences in southern New Mexico was tested several times over a period of nine months. Environmental effects common to this location that could potentially produce changes in the measured modal properties include changes in temperature, high winds, and changes to the supporting soil medium. In addition to environmental effects, variabilities in modal testing procedures and data reduction can also cause changes in the identified dynamic properties of the structure. In this paper the natural variability of the frequencies and mode shapes of the Alamosa Canyon bridge that result from changes in time of day when the test was performed, amount of traffic, and environmental conditions will be discussed. Because this bridge has not been in active use throughout the testing period, it is assumed that any change in the observed modal properties are the result of the factors listed above rather than deterioration of the structure itself.

Design and performance validation of a wireless sensing unit for structural monitoring applications

Abstract: There exists a clear need to monitor the performance of civil structures over their operational lives. Current commercial monitoring systems suffer from various technological and economic limitations that prevent their widespread adoption. The wires used to route measurements from system sensors to the centralized data server represent one of the greatest limitations since they are physically vulnerable and expensive from an installation and maintenance standpoint. In lieu of cables, the introduction of low-cost wireless communications is proposed. The result is the design of a prototype wireless sensing unit that can serve as the fundamental building block of wireless modular monitoring systems (WiMMS). An additional feature of the wireless sensing unit is the incorporation of computational power in the form of state-of-art microcontrollers. The prototype unit is validated with a series of laboratory and field tests. The Alamosa Canyon Bridge is employed to serve as a full-scale benchmark structure to validate the performance of the wireless sensing unit in the field. A traditional cable-based monitoring system is installed in parallel with the wireless sensing units for performance comparison.

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.