CONFERENCE PROCEEDINGS
Advanced Search >
Home > Proceedings > Volume 8345 > Article
Translator Disclaimer
6 April 2012 Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage
Hae Young Noh; Ram Rajagopal; Anne S. Kiremidjian
Author Affiliations +
Proceedings Volume 8345, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012; 834507 (2012) https://doi.org/10.1117/12.915409
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2012, San Diego, California, United States
Abstract
This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Hae Young Noh, Ram Rajagopal, and Anne S. Kiremidjian "Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage", Proc. SPIE 8345, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012, 834507 (6 April 2012); doi: 10.1117/12.915409; https://doi.org/10.1117/12.915409
ACCESS THE FULL ARTICLE
PERSONAL SIGN IN
Full access may be available with your subscription
 
Forgot your username?
Forgot your password?
No SPIE account? Create an account
or Institutional Sign In via Shibboleth
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $14.40
Non-members: $18.00 ADD TO CART
PROCEEDINGS
 13 PAGES
DOWNLOAD PDF SAVE TO MY LIBRARY
SHARE
GET CITATION
< Previous Article
|
Next Article >
Advertisement
Advertisement
KEYWORDS
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top