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Dustin Harvey
Researcher at Los Alamos National Laboratory
Publications - 17
Citations - 125
Dustin Harvey is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Structural health monitoring & Genetic programming. The author has an hindex of 5, co-authored 17 publications receiving 105 citations. Previous affiliations of Dustin Harvey include University of California, San Diego.
Papers
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Journal ArticleDOI
Automated Feature Design for Numeric Sequence Classification by Genetic Programming
Dustin Harvey,Michael D. Todd +1 more
TL;DR: This paper proposes a novel genetic programming (GP) approach to automated feature design called Autofead, a GP variant evolves a population of candidate features built from a library of sequence-handling functions to leverage the power of both numerical optimization and standard pattern recognition algorithms.
Book ChapterDOI
Characterization and Prognosis of Multirotor Failures
TL;DR: A comprehensive sensor network was successfully designed and implemented on an MR vehicle, and a compatible set of tools was developed for signal processing, and the resulting SHM system is capable of classifying propeller, motor, and structural hardware failures.
Proceedings ArticleDOI
SHMTools: a new embeddable software package for SHM applications
Eric B. Flynn,Samory Kpotufe,Dustin Harvey,Eloi Figueiredo,Stuart G. Taylor,Stuart G. Taylor,Denis Dondi,Todor Mollov,Michael D. Todd,Tajana Rosing,Gyuhae Park,Charles R. Farrar +11 more
TL;DR: A new software package, SHMTools, for prototyping algorithms for various structural health monitoring (SHM) applications, which includes a set of standardized MATLAB routines covering three main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification.
Journal ArticleDOI
Structural health monitoring feature design by genetic programming
Dustin Harvey,Michael D. Todd +1 more
TL;DR: Experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring is provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.
Book ChapterDOI
Towards Population-Based Structural Health Monitoring, Part V: Networks and Databases
Chandula T. Wickramarachchi,Daniel S. Brennan,Weijiang Lin,Eoghan Maguire,Dustin Harvey,Elizabeth J. Cross,Keith Worden +6 more
TL;DR: In this article, the most important aspects of using databases in population-based Structural Health Monitoring (SHM) and the exploitation of the unique Echo framework, providing a platform for diagnostics across populations of wind turbines are discussed.