Chuck Farrar is currently the director of Los Alamos National Laboratory’s (LANL) Engineering Institute, a research and education collaboration between LANL and the Univ. of California San Diego’s (UCSD) Jacobs School of Eng. His research interests focus on developing integrated hardware and software solutions to structural health monitoring (SHM) and damage prognosis problems. The results of this research have been documented in over 400 publications and most recently in a book entitled Structural Health Monitoring A Machine Learning Perspective. His work has been recognized at LANL through his reception of the inaugural Los Alamos Fellows Prize for Technical Leadership and by the SHM community through the reception of the inaugural Lifetime Achievement Award in Structural Health Monitoring. Each year he teaches a graduate course on SHM for UCSD. Additional professional activities include the development of a structural health monitoring short course that has been offered more than 35 times to industry and government agencies in Asia, Australia, Europe and the U.S. In 2012 he was elected as a Fellow of Los Alamos National Laboratory. He is also a Fellow of the American Society of Mechanical Engineers, the American Society of Civil Engineers and the Society for Experimental Mechanics.
Structural Health Monitoring: Historical Development, Current Status, Research Needs
The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The SHM process compliments traditional nondestructive evaluation by extending these concepts to online, in situ system monitoring on a more global scale. For long term SHM, the output of this process is periodically updated information regarding the ability of the structure to perform its intended function in light of the inevitable aging and degradation resulting from operational environments. After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening and aims to provide, in near real time, reliable information regarding the integrity of the structure.
This presentation will briefly summarize the historical developments of SHM technology, which have been driven by four applications: rotating machinery, offshore oil platforms, civil infrastructure, and aerospace structures. Next, the current state of the art is summarized where the SHM problem is described in terms of a statistical pattern recognition paradigm. In this paradigm, the SHM process can be broken down into four parts: (1) Operational Evaluation, (2) Data
Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. Examples related to each of these areas will be cited. Next, Outstanding research issues are discussed in the context of this paradigm along with examples of current research being undertaken at Los Alamos National Laboratory’s Engineering
Institute that attempts to address some of these outstanding research issues. This talk will conclude with some final comments on the role of SHM in the goal of providing cradle-to-grave system state awareness, which is a grand challenge for engineers in the 21st century.