Matching Strain Signals to Structural Operational Conditions by Means of an Improved Genetic Density Based Algorithm

Juan Carlos Perafan Lopez, Julian Sierra Perez

Research output: Contribution to journalArticle in an indexed scientific journalpeer-review

Abstract

Producto derivado del proyecto con radicado 636B-06/16-57
Structural health monitoring SHM is highly relevant nowadays, not only for aerospace maintenance, but also there is a large number of newly applications in which this methodology is involved, like in the civil and mechanical fields for structure operational conservation. Pattern recognition has become an important part of SHM for signal processing and anomalies or damage detection, assuring structural integrity. New methods are created day by day and more researches and engineers feel the interest to generate techniques which can make SHM become a more compacted, sophisticated and automatized system, eliminating human factors and intrinsic errors. This work evaluates a novelty methodology as a part of the SHM in which it is used an unsupervised clustering algorithm based on density DBSCAN, to facilitate the detection and interpretation of structural behaviors, with low computational cost and processing time and fairly accuracy. Index Terms - Clustering, Pattern Recognition, Shm, Unsupervised Learning And Structural Evaluation.
Original languageSpanish (Colombia)
Pages (from-to)70-75
JournalInternational Journal of Mechanical and Production Engineering (IJMPE)
Volume6
Issue number5
StatePublished - 9 Aug 2018

Types Minciencias

  • Artículos de investigación con calidad D

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