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Volumn 17, Issue 6, 2008, Pages

The application of statistical pattern recognition methods for damage detection to field data

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; PATTERN RECOGNITION; STRUCTURAL HEALTH MONITORING;

EID: 58149463472     PISSN: 09641726     EISSN: 1361665X     Source Type: Journal    
DOI: 10.1088/0964-1726/17/6/065023     Document Type: Article
Times cited : (65)

References (16)
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  • 3
    • 0003285688 scopus 로고    scopus 로고
    • Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review
    • Doebling S W, Farrar C R, Prime M B and Shevitz D W 1996 Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review Los Alamos National Laboratory Report LA-13070-MS
    • (1996) Los Alamos National Laboratory Report
    • Doebling S W1    Farrar C R2    Prime M B3    Shevitz D W4
  • 7
    • 0037250871 scopus 로고    scopus 로고
    • Experimental validation of structural health monitoring methodology II: novelty detection on a laboratory structure
    • Manson G, Worden K and Allman D J 2003a Experimental validation of structural health monitoring methodology II: novelty detection on a laboratory structure J. Sound Vib. 259 345-63
    • (2003) J. Sound Vib. , vol.259 , Issue.2 , pp. 345-363
    • Manson G1    Worden K2    Allman D J3
  • 8
    • 0037247230 scopus 로고    scopus 로고
    • Experimental validation of structural health monitoring methodology III: novelty detection on a laboratory structure
    • Manson G, Worden K and Allman D J 2003b Experimental validation of structural health monitoring methodology III: novelty detection on a laboratory structure J. Sound Vib. 259 365-85
    • (2003) J. Sound Vib. , vol.259 , Issue.2 , pp. 365-385
    • Manson G1    Worden K2    Allman D J3
  • 9
    • 34250898495 scopus 로고    scopus 로고
    • Time series based structural damage detection algorithm using gaussian mixtures modeling
    • Nair K K and Kiremidjian A S 2007 Time series based structural damage detection algorithm using gaussian mixtures modeling ASME J. Dyn. Syst. Meas. Control 129 258-93
    • (2007) ASME J. Dyn. Syst. Meas. Control , vol.129 , Issue.3 , pp. 285-293
    • Nair K K1    Kiremidjian A S2
  • 10
    • 30344437620 scopus 로고    scopus 로고
    • Time series based damage detection and localization algorithm with application to the ASCE benchmark structure
    • Nair K K, Kiremidjian A S and Law K H 2006 Time series based damage detection and localization algorithm with application to the ASCE benchmark structure J. Sound Vib. 291 349-68
    • (2006) J. Sound Vib. , vol.291 , Issue.1-2 , pp. 349-368
    • Nair K K1    Kiremidjian A S2    Law K H3
  • 12
    • 0001252638 scopus 로고    scopus 로고
    • Structural health monitoring using statistical pattern recognition techniques
    • Sohn H, Farrar C R, Hunter N F and Worden K 2001b Structural health monitoring using statistical pattern recognition techniques ASME J. Dyn. Syst. Meas. Control 123 706-11 (Special issue on Identification of Mechanical Systems)
    • (2001) ASME J. Dyn. Syst. Meas. Control , vol.123 , Issue.4 , pp. 706-711
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  • 13
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    • Straser E G and Kiremidjian A S 1998 A modular wireless damage monitoring system forstructures Report No. 128 (John A Blume Earthquake Engineering Center, Stanford, CA)
    • (1998) Report No. 128
    • Straser E G Kiremidjian A S1
  • 14
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    • Wenzel H 2005 personal communication
    • (2005)
    • Wenzel H1
  • 15
    • 0037248049 scopus 로고    scopus 로고
    • Experimental validation of structural health monitoring methodology I: novelty detection on a laboratory structure
    • Worden K, Manson G and Allman D J 2003 Experimental validation of structural health monitoring methodology I: novelty detection on a laboratory structure J. Sound Vib. 259 323-43
    • (2003) J. Sound Vib. , vol.259 , Issue.2 , pp. 323-343
    • Worden K1    Manson G2    Allman D J3
  • 16
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    • Damage detection using outlier analysis
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.