A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition. Issue 3 (12th February 2015)
- Record Type:
- Journal Article
- Title:
- A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition. Issue 3 (12th February 2015)
- Main Title:
- A novel bridge structure damage diagnosis algorithm based on post‐nonlinear ICA and statistical pattern recognition
- Authors:
- Xiao, Haitao
Lou, Sheng
Ogai, Harutoshi - Abstract:
- <abstract abstract-type="main" id="tee22085-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="tee22085-para-0004">Monitoring the health of bridges and diagnosing the damage is vital for government and related institutions in Japan because of frequent earthquakes and the oceanic climate. This paper develops a bridge structure health monitoring system (BSHM), which includes data acquisition and analysis. A two‐stage structure damage detection algorithm based on post‐nonlinear independent component analysis (ICA) and statistical pattern recognition is proposed to analyze the acquired data and evaluate the health of bridges. First, an improved post‐nonlinear ICA algorithm is proposed for denoising, and a data‐sample matching based data normalization scheme to reduce the effect of varying environmental and operational condition. Thereafter, fast Fourier transform (FFT) is used to detect the damage. Based on the first stage, a statistical pattern recognition damage detection algorithm, including a new damage sensitive index <italic>D</italic><sub><italic>SPR</italic></sub>, is proposed to determine the severity and location(s) of damage. In addition to the algorithm, this paper presents several simulations and experiments, including a detection experiment that applies artificial damage to a real bridge to show that our design choices are indeed effective. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.</p><abstract abstract-type="main" id="tee22085-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="tee22085-para-0004">Monitoring the health of bridges and diagnosing the damage is vital for government and related institutions in Japan because of frequent earthquakes and the oceanic climate. This paper develops a bridge structure health monitoring system (BSHM), which includes data acquisition and analysis. A two‐stage structure damage detection algorithm based on post‐nonlinear independent component analysis (ICA) and statistical pattern recognition is proposed to analyze the acquired data and evaluate the health of bridges. First, an improved post‐nonlinear ICA algorithm is proposed for denoising, and a data‐sample matching based data normalization scheme to reduce the effect of varying environmental and operational condition. Thereafter, fast Fourier transform (FFT) is used to detect the damage. Based on the first stage, a statistical pattern recognition damage detection algorithm, including a new damage sensitive index <italic>D</italic><sub><italic>SPR</italic></sub>, is proposed to determine the severity and location(s) of damage. In addition to the algorithm, this paper presents several simulations and experiments, including a detection experiment that applies artificial damage to a real bridge to show that our design choices are indeed effective. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.</p> </abstract> … (more)
- Is Part Of:
- IEEJ transactions on electrical and electronic engineering. Volume 10:Issue 3(2015)
- Journal:
- IEEJ transactions on electrical and electronic engineering
- Issue:
- Volume 10:Issue 3(2015)
- Issue Display:
- Volume 10, Issue 3 (2015)
- Year:
- 2015
- Volume:
- 10
- Issue:
- 3
- Issue Sort Value:
- 2015-0010-0003-0000
- Page Start:
- 287
- Page End:
- 300
- Publication Date:
- 2015-02-12
- Subjects:
- Electrical engineering -- Periodicals
Electronics -- Periodicals
621.3 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/tee.22085 ↗
- Languages:
- English
- ISSNs:
- 1931-4973
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4363.240505
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3591.xml