Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Structural response recovery based on improved multi-scale principal component analysis considering sensor performance degradation

    Access Status
    Fulltext not available
    Authors
    Ma, S.
    Li, Jun
    Hao, Hong
    Jiang, S.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Ma, S. and Li, J. and Hao, H. and Jiang, S. 2018. Structural response recovery based on improved multi-scale principal component analysis considering sensor performance degradation. Advances in Structural Engineering. 21 (2): pp. 241-255.
    Source Title
    Advances in Structural Engineering
    DOI
    10.1177/1369433217717114
    ISSN
    1369-4332
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/61302
    Collection
    • Curtin Research Publications
    Abstract

    This article proposes an improved multi-scale principal component analysis approach to recover the structural dynamic response in the time domain from data measured by performance-deteriorated sensors. The proposed approach builds on discrete wavelet transform, principal component analysis, and inverse discrete wavelet transform. The main approach is based on a cross-correlation calculation to better preserve the true structural vibration behavior as well as to recover the structural response from data measured by deteriorated sensors. Three sensor performance degradation models are considered in this study. Numerical studies are conducted first to demonstrate the effectiveness and accuracy of the proposed approach to recover the structural vibration response of an example spatial truss model considering sensor performance degradation under different loading scenarios. Experimental data on an in-field benchmark bridge are also analyzed to further validate and demonstrate the performance of the proposed approach.

    Related items

    Showing items related by title, author, creator and subject.

    • Structural damage detection considering sensor performance degradation and measurement noise effect
      Ma, S.; Jiang, S.; Li, Jun (2019)
      In the real civil structures, material deterioration, overloading and environmental corrosion inevitably lead to sensor performance degradation or sensor fault. Sensor performance degradation or sensor fault usually ...
    • Damage Identification and Optimal Sensor Placement for Structures under Unknown Traffic-Induced Vibrations
      Li, Jun; Hao, Hong; Chen, Z. (2015)
      This paper proposes a damage-identification and optimal sensor-placement approach for structures under unknown traffic-induced vibrations. Response reconstruction is performed for structures under traffic-induced vibrations ...
    • Structural damage identification with extracted impulse response functions and optimal sensor locations
      Li, Jun; Hao, Hong; Fan, X. (2015)
      This paper presents a structural damage identification approach based on the time domain impulse response functions, which are extracted from the measured dynamic responses with the input available. The theoretical ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.