Engineering Institute Leader
Los Alamos National Laboratory
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. The LANL-UCSD Engineering Institute accomplishments in 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.