Full Content is available to subscribers

Subscribe/Learn More  >

On-Line Structural Damage Feature Extraction Based on Autoregressive Statistical Pattern of Time Series

[+] Author Affiliations
Ming-Hui Hu

East China University of Science and Technology, Shanghai, ChinaUniversity of California, Berkeley, CA

Shan-Tung Tu, Fu-Zhen Xuan, Zheng-Dong Wang

East China University of Science and Technology, Shanghai, China

Paper No. PVP2014-28142, pp. V005T10A009; 4 pages
  • ASME 2014 Pressure Vessels and Piping Conference
  • Volume 5: High-Pressure Technology; ASME NDE Division; 22nd Scavuzzo Student Paper Symposium and Competition
  • Anaheim, California, USA, July 20–24, 2014
  • Conference Sponsors: Pressure Vessels and Piping Division
  • ISBN: 978-0-7918-4602-5
  • Copyright © 2014 by ASME


The main aim of this paper is to demonstrate an autoregressive statistical pattern analysis method for the on-line structural health monitoring based on the damage feature extraction. The strain signals obtained from sensors are modeled as autoregressive moving average (ARMA) time series to extract the damage sensitive features (DSF) to monitor the variations of the selected features. One algebra combination of the first three AR coefficients is defined as damage sensitive feature. Using simple theory of polynomial roots, the relationship between the first three AR coefficient and the roots of the characteristic equation of the transfer function is deduced. Structural damage detection is conducted by comparing the DSF values of the inspected structure. The corresponding damage identification experiment was investigated in X12CrMoWVNbN steel commonly used for rotor of steam turbine in power plants. The feasibility and validity of the proposed method are shown.

Copyright © 2014 by ASME



Interactive Graphics


Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In