An optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics

M. A. Torres-Arredondo, Julián Sierra-Pérez, Guénaël Cabanes

    Research output: Contribution to journalArticlepeer-review

    7 Scopus citations

    Abstract

    The process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). For the design of a trustworthy health monitoring system, a vast amount of information regarding the inherent physical characteristics of the sources and their propagation and interaction across the structure is crucial. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organising maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and identifiable.

    Original languageEnglish
    Article number055034
    JournalSmart Materials and Structures
    Volume25
    Issue number5
    DOIs
    StatePublished - 13 Apr 2016

    Bibliographical note

    Publisher Copyright:
    © 2016 IOP Publishing Ltd.

    Keywords

    • acousto-ultrasonics
    • damage detection
    • data-driven modeling
    • density-based simultaneous two-level-self organising map
    • temperature compensation

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