Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

Structural Health Monitoring

Volumn 13, Issue 4, 2014, Pages 461-472

  Nguyen, Theanh ^a   Chan, Tommy H T ^a   Thambiratnam, David P ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

data condition assessment; data generation; Mahalanobis squared distance; Monte Carlo; multivariate normal (multinormal); novelty detection; Statistical damage identification

Indexed keywords

BENCHMARKING; MONTE CARLO METHODS; SENSORS;

CONDITION ASSESSMENTS; DAMAGE IDENTIFICATION; DATA GENERATION; MULTINORMAL; NOVELTY DETECTION; SQUARED DISTANCES;

DAMAGE DETECTION;

EID: 84903946292     PISSN: 14759217     EISSN: 17413168     Source Type: Journal    
DOI: 10.1177/1475921714521270     Document Type: Article

References (27)

1. Farrar CR,Sohn H,Worden K.Data normalization: a key for structural health monitoring. Proceedings of the 3rd international workshop on structural health monitoring; Stanford, CA;. 1229.
2. Sohn H,Farrar CR,Hemez F, et alA review of structural health monitoring literature: 1996-2001. Los Alamos, NM: Los Alamos National Laboratory; 2003:.
3. Manson G,Worden K,Allman D.Experimental validation of a structural health monitoring methodology: Part II. Novelty detection on a Gnat aircraft.J Sound Vib. 2003;259 (2): 345-363.
4. Figueiredo E,Park G,Farrar CRet al.Machine learning algorithms for damage detection under operational and environmental variability.Struct Health Monit. 2011;10 (6): 559-572.
5. Farrar CR,Worden KStructural health monitoring: a machine learning perspective. Chichester: John Wiley & Sons; 2013:.
6. Worden K,Manson G,Fieller NRJ.Damage detection using outlier analysis.J Sound Vib. 2000;229 (3): 647-667.
7. Figueiredo E,Park G,Figueiras J, et alStructural health monitoring algorithm comparisons using standard data sets. Los Alamos, NM: Los Alamos National Laboratory; 2009:.
8. Filzmoser P,Garrett RG,Reimann C.Multivariate outlier detection in exploration geochemistry.Comput Geosci. 2005;31 (5): 579-587.
9. Johnson RA,Wichern DWApplied multivariate statistical analysis. 5th ed.Upper Saddle River, NJ: Prentice Hall, 2002.Upper Saddle River, NJ: Prentice Hall; 2002:.
10. Rencher ACMethods of multivariate analysis. 2nd ed.New York: John Wiley & Sons, 2002.New York: John Wiley & Sons; 2002:.
11. Farrar CR,Doebling SW,Nix DA.Vibration-based structural damage identification.Philos T Roy Soc A. 2001;359 (1778): 131-149.
12. Worden K,Manson G.The application of machine learning to structural health monitoring.Philos T Roy Soc A. 2007;365 (1851): 515-537.
13. Nguyen T,Chan THT,Thambiratnam DP.Effects of wireless sensor network uncertainties on output-only modal-based damage identification.Aust J Struct Eng. 2014;15 (1): 15-25.
14. Gul M,Catbas FN.Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications.Mech Syst Signal Pr. 2009;23 (7): 2192-2204.
15. Worden K,Sohn H,Farrar CR.Novelty detection in a changing environment regression and interpolation approaches.J Sound Vib. 2002;258 (4): 741-761.
16. Sohn H,Farrar C,Hunter N, et alApplying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat. Los Alamos, NM: Los Alamos National Laboratory; 2001:.
17. Martinez WL,Martinez ARComputational statistics handbook with MATLAB. Boca Raton, FL: CRC Press; 2002:.
18. Wikipedia. Monte Carlo method, http://en.wikipedia.org/wiki/Monte_Carlo_method (2002, accessed August 2012).
19. Dunn WL,Shultis JKExploring Monte Carlo methods. Burlington, VT: Elsevier; 2011:.
20. Worden K,Farrar CR,Manson Get al.The fundamental axioms of structural health monitoring.P Roy Soc A: Math Phy. 2007;463 (2082): 1639-1664.
21. Golub GH,Van Loan CFMatrix computations. 3rd ed.Baltimore, MD: Johns Hopkins University Press, 1996.Baltimore, MD: Johns Hopkins University Press; 1996:.
22. Strang GLinear algebra and its applications. 4th ed.Belmont, CA: Thomson Brooks/Cole, 2006.Belmont, CA: Thomson Brooks/Cole; 2006:.
23. Sharma SApplied multivariate techniques. New York: John Wiley & Sons; 1995:.
24. Martinez WL,Martinez ARExploratory data analysis with MATLAB. Boca Raton, FL: Chapman & Hall/CRC; 2005:.
25. Los Alamos National Laboratory. SHMTools and mFUSE, http://institute.lanl.gov/ei/software-and-data/SHMTools/ (2010, accessed August 2012).
26. Ljung LSystem identification toolbox™ 7 user's guide. Natick, MA: MathWorks; 2011:.
27. Statistics toolbox™ 7 user's guide. Natick, MA: MathWorks; 2011:.