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.

SHMTools: a new embeddable software package for SHM applications

Abstract: This paper describes a new software package, SHMTools, for prototyping algorithms for various structural health monitoring (SHM) applications. The software includes a set of standardized MATLAB routines covering three main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification. A subset of the software in SHMTools is embeddable, which consists of Matlab functions that can be cross-compiled into generic "C" programs to be run on a target hardware. The software is designed to accommodate multiple sensing modalities, including piezoelectric active-sensing, which have become widely used in SHM practice. The software package, standardized datasets, and detailed documentation are publicly available for use by the SHM community. The details of this software will be discussed, along with several example processes to demonstrate its utility.

The application of compressed sensing to detecting damage in structures

Abstract: One of the principal challenges facing the structural health monitoring (SHM) community is taking large, heterogeneous sets of data collected from sensors, and extracting information that allows the estimation of the remaining service life of a structure. Another important challenge is to collect relevant data from a structure in a manner that is cost effective, and respects the size, weight, cost, energy consumption, and bandwidth limitations placed on the system. In this work we explore the suitability of compressed sensing to address both challenges. In this work a digital version of a compressed sensor is implemented on-board a microcontroller similar to those used in embedded SHM sensor nodes. The sensor node is tested in a surrogate SHM application requiring acceleration measurements. Currently the prototype compressed sensor is capable of collecting compressed coefficients from measurements and sending them to an off-board processor for reconstruction using L1 norm minimization. A compressed version of the matched filter known as the smashed filter, has also been implemented on-board the sensor node, and its suitability for detecting structural damage will be discussed.

Developing impact and fatigue damage prognosis solutions for composites

Abstract: An approach to developing a damage prognosis solution that integrates advanced sensing technology, data interrogation procedures for damage detection, novel model validation and uncertainty quantification techniques, and reliability-based decision-making algorithms is summarized in this article. In parallel, experimental efforts are underway to deliver a proof-of-principle technology demonstration by assessing impact damage and predicting the subsequent fatigue damage accumulation in a composite plate. This article provides an overview of the various technologies that are being integrated to address this damage prognosis problem.

Field Verification of a Nondestructive Damage Location Algorithm

Abstract: Over the past 25 years, the use of modal parameters for detecting damage has received considerable attention from the civil engineering community. The basic idea is that changes in the structure`s properties, primarily stiffness, will alter the dynamic properties of the structure such as frequencies and mode shapes, and properties derived from these quantities such as modal-based flexibility. In this paper, a method for nondestructive damage location in bridges, as determined by changes in the modal properties, is described. The damage detection algorithm is applied to pre- and post-damage modal properties measured on a bridge. Results of the analysis indicate that the method accurately locates the damage. Subjects relating to practical implementation of this damage identification algorithm that need further study are discussed.

Piezoelectric Structural Excitation using a Wireless Active Sensing Unit

Abstract: Strong interest in applying wireless sensing technologies within structural monitoring systems has grown in recent years. Wireless sensors are capable of passively collecting response measurements of a dynamic structural system at low-costs. However, the role of wireless sensing within structural monitoring systems can be expanded if sensors are provided a direct interface to the physical system in which they are installed. Capable of exciting a structural system through actuators, a wireless “active” sensor would be a valuable tool in structural control and damage detection applications. In this study, a wireless active sensing unit is proposed and fabricated. After fabrication of a prototype, a series of validation tests are conducted to assess the unit’s performance. A piezoelectric pad mounted to an aluminum plate is commanded by the wireless active sensing unit to impart lowenergy Lamb waves in the plate surface. Simultaneously, the same unit collects response measurements obtained from a second piezoelectric pad also surface mounted to the plate. To illustrate the potential of the wireless active sensing unit to locally perform system identification analyses, the computational core is provided the task of calculating autoregressive time-series models using input-output time-history data collected from the excited system.

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.