Damage identification for composite structures with a Bayesian network

Minh Nguyen; Xiaoming Wang; Zhongqing Su; Lin Ye

Damage identification for composite structures with a Bayesian network

Minh Nguyen, Xiaoming Wang, Zhongqing Su, Lin Ye

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

9 Citations (Scopus)

Abstract

Recent development on the application of distributed sensor networks in Structural Health Monitoring (SHM) for large structural areas has resulted in more complicated system identification techniques, particularly for those with multiple information sources. This paper presents an application of Bayesian inference network to detection of hole-type damages on a composite plate using multiple sensing data streams from a distributed sensor network. Representative damage features from 50 damage scenarios were used for the learning process. The Bayesian net is found to be promising when correctly diagnosing the damage's location and size for a validation case.

Original language	English
Title of host publication	Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04
Pages	307-311
Number of pages	5
Publication status	Published - 1 Dec 2004
Externally published	Yes
Event	2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04 - Melbourne, Australia Duration: 14 Dec 2004 → 17 Dec 2004

Conference

Conference	2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04
Country/Territory	Australia
City	Melbourne
Period	14/12/04 → 17/12/04

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Damage identification for composite structures with a Bayesian network",

abstract = "Recent development on the application of distributed sensor networks in Structural Health Monitoring (SHM) for large structural areas has resulted in more complicated system identification techniques, particularly for those with multiple information sources. This paper presents an application of Bayesian inference network to detection of hole-type damages on a composite plate using multiple sensing data streams from a distributed sensor network. Representative damage features from 50 damage scenarios were used for the learning process. The Bayesian net is found to be promising when correctly diagnosing the damage's location and size for a validation case.",

author = "Minh Nguyen and Xiaoming Wang and Zhongqing Su and Lin Ye",

year = "2004",

month = dec,

day = "1",

language = "English",

isbn = "0780388933",

pages = "307--311",

booktitle = "Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04",

note = "2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04 ; Conference date: 14-12-2004 Through 17-12-2004",

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Damage identification for composite structures with a Bayesian network. / Nguyen, Minh; Wang, Xiaoming; Su, Zhongqing et al.
Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04. 2004. p. 307-311.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Damage identification for composite structures with a Bayesian network

AU - Nguyen, Minh

AU - Wang, Xiaoming

AU - Su, Zhongqing

AU - Ye, Lin

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Recent development on the application of distributed sensor networks in Structural Health Monitoring (SHM) for large structural areas has resulted in more complicated system identification techniques, particularly for those with multiple information sources. This paper presents an application of Bayesian inference network to detection of hole-type damages on a composite plate using multiple sensing data streams from a distributed sensor network. Representative damage features from 50 damage scenarios were used for the learning process. The Bayesian net is found to be promising when correctly diagnosing the damage's location and size for a validation case.

AB - Recent development on the application of distributed sensor networks in Structural Health Monitoring (SHM) for large structural areas has resulted in more complicated system identification techniques, particularly for those with multiple information sources. This paper presents an application of Bayesian inference network to detection of hole-type damages on a composite plate using multiple sensing data streams from a distributed sensor network. Representative damage features from 50 damage scenarios were used for the learning process. The Bayesian net is found to be promising when correctly diagnosing the damage's location and size for a validation case.

UR - http://www.scopus.com/inward/record.url?scp=28844502804&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SN - 0780388933

SN - 9780780388932

SP - 307

EP - 311

BT - Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04

T2 - 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, ISSNIP '04

Y2 - 14 December 2004 through 17 December 2004

ER -

Damage identification for composite structures with a Bayesian network

Abstract

Conference

ASJC Scopus subject areas

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