Bridge diagnosis system by using nonlinear independent component analysis

Juanqing Zheng, Qingwen Wang, Harutoshi Ogai, Chen Shao, Jingqiu Huang

研究成果: Conference contribution

1 引用 (Scopus)

抄録

The aim of this paper is to structure a daily diagnosis system for bridge monitoring and maintenance. Technology of wireless sensor, signal processing and structure analysis are used in this diagnosis system. The vibration data are collected through wireless sensor network by exerting some external forces such as running vehicles. Then use nonlinear independent component analysis (nonlinear ICA) and spectral analysis to analyze the data for extracting character frequency. In the past, linear ICA such as FastICA is used to do the signal processing step. But simple linear ICA algorithms work efficiently only in linear mixing environments. Whereas a nonlinear ICA model, which is more complicated, would be more practical for bridge diagnosis system. In this paper, we firstly use post nonlinear (PNL) method to change the data from nonlinear to linear, after that do linear separation by FastICA. Through the processed data this diagnosis technology can be used to understand the phenomena like corrosion and crack and evaluate the health condition of a bridge. We apply this system to do experiments at Nakajima Bridge in Yahata, Kitakyushu, Japan and successfully extract the character frequency of a bridge.

元の言語English
ホスト出版物のタイトルProceedings of the SICE Annual Conference
ページ2118-2121
ページ数4
出版物ステータスPublished - 2010
イベントSICE Annual Conference 2010, SICE 2010 - Taipei
継続期間: 2010 8 182010 8 21

Other

OtherSICE Annual Conference 2010, SICE 2010
Taipei
期間10/8/1810/8/21

Fingerprint

Independent component analysis
Signal processing
Spectrum analysis
Wireless sensor networks
Health
Corrosion
Cracks
Monitoring
Sensors
Experiments

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

これを引用

Zheng, J., Wang, Q., Ogai, H., Shao, C., & Huang, J. (2010). Bridge diagnosis system by using nonlinear independent component analysis. : Proceedings of the SICE Annual Conference (pp. 2118-2121). [5604138]

Bridge diagnosis system by using nonlinear independent component analysis. / Zheng, Juanqing; Wang, Qingwen; Ogai, Harutoshi; Shao, Chen; Huang, Jingqiu.

Proceedings of the SICE Annual Conference. 2010. p. 2118-2121 5604138.

研究成果: Conference contribution

Zheng, J, Wang, Q, Ogai, H, Shao, C & Huang, J 2010, Bridge diagnosis system by using nonlinear independent component analysis. : Proceedings of the SICE Annual Conference., 5604138, pp. 2118-2121, SICE Annual Conference 2010, SICE 2010, Taipei, 10/8/18.
Zheng J, Wang Q, Ogai H, Shao C, Huang J. Bridge diagnosis system by using nonlinear independent component analysis. : Proceedings of the SICE Annual Conference. 2010. p. 2118-2121. 5604138
Zheng, Juanqing ; Wang, Qingwen ; Ogai, Harutoshi ; Shao, Chen ; Huang, Jingqiu. / Bridge diagnosis system by using nonlinear independent component analysis. Proceedings of the SICE Annual Conference. 2010. pp. 2118-2121
@inproceedings{b767fe81d781481c826074f805b1a5d6,
title = "Bridge diagnosis system by using nonlinear independent component analysis",
abstract = "The aim of this paper is to structure a daily diagnosis system for bridge monitoring and maintenance. Technology of wireless sensor, signal processing and structure analysis are used in this diagnosis system. The vibration data are collected through wireless sensor network by exerting some external forces such as running vehicles. Then use nonlinear independent component analysis (nonlinear ICA) and spectral analysis to analyze the data for extracting character frequency. In the past, linear ICA such as FastICA is used to do the signal processing step. But simple linear ICA algorithms work efficiently only in linear mixing environments. Whereas a nonlinear ICA model, which is more complicated, would be more practical for bridge diagnosis system. In this paper, we firstly use post nonlinear (PNL) method to change the data from nonlinear to linear, after that do linear separation by FastICA. Through the processed data this diagnosis technology can be used to understand the phenomena like corrosion and crack and evaluate the health condition of a bridge. We apply this system to do experiments at Nakajima Bridge in Yahata, Kitakyushu, Japan and successfully extract the character frequency of a bridge.",
keywords = "Bridge diagnosis system, Independent component analysis, Post nonlinear method, Wireless sensor network",
author = "Juanqing Zheng and Qingwen Wang and Harutoshi Ogai and Chen Shao and Jingqiu Huang",
year = "2010",
language = "English",
isbn = "9784907764364",
pages = "2118--2121",
booktitle = "Proceedings of the SICE Annual Conference",

}

TY - GEN

T1 - Bridge diagnosis system by using nonlinear independent component analysis

AU - Zheng, Juanqing

AU - Wang, Qingwen

AU - Ogai, Harutoshi

AU - Shao, Chen

AU - Huang, Jingqiu

PY - 2010

Y1 - 2010

N2 - The aim of this paper is to structure a daily diagnosis system for bridge monitoring and maintenance. Technology of wireless sensor, signal processing and structure analysis are used in this diagnosis system. The vibration data are collected through wireless sensor network by exerting some external forces such as running vehicles. Then use nonlinear independent component analysis (nonlinear ICA) and spectral analysis to analyze the data for extracting character frequency. In the past, linear ICA such as FastICA is used to do the signal processing step. But simple linear ICA algorithms work efficiently only in linear mixing environments. Whereas a nonlinear ICA model, which is more complicated, would be more practical for bridge diagnosis system. In this paper, we firstly use post nonlinear (PNL) method to change the data from nonlinear to linear, after that do linear separation by FastICA. Through the processed data this diagnosis technology can be used to understand the phenomena like corrosion and crack and evaluate the health condition of a bridge. We apply this system to do experiments at Nakajima Bridge in Yahata, Kitakyushu, Japan and successfully extract the character frequency of a bridge.

AB - The aim of this paper is to structure a daily diagnosis system for bridge monitoring and maintenance. Technology of wireless sensor, signal processing and structure analysis are used in this diagnosis system. The vibration data are collected through wireless sensor network by exerting some external forces such as running vehicles. Then use nonlinear independent component analysis (nonlinear ICA) and spectral analysis to analyze the data for extracting character frequency. In the past, linear ICA such as FastICA is used to do the signal processing step. But simple linear ICA algorithms work efficiently only in linear mixing environments. Whereas a nonlinear ICA model, which is more complicated, would be more practical for bridge diagnosis system. In this paper, we firstly use post nonlinear (PNL) method to change the data from nonlinear to linear, after that do linear separation by FastICA. Through the processed data this diagnosis technology can be used to understand the phenomena like corrosion and crack and evaluate the health condition of a bridge. We apply this system to do experiments at Nakajima Bridge in Yahata, Kitakyushu, Japan and successfully extract the character frequency of a bridge.

KW - Bridge diagnosis system

KW - Independent component analysis

KW - Post nonlinear method

KW - Wireless sensor network

UR - http://www.scopus.com/inward/record.url?scp=78649255339&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649255339&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78649255339

SN - 9784907764364

SP - 2118

EP - 2121

BT - Proceedings of the SICE Annual Conference

ER -