A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

Advances in Engineering Software

Volumn 42, Issue 6, 2011, Pages 368-374

  Jiang, Shao Fei ^a   Fu, Chun ^a,b   Zhang, Chunming ^c  

^a FUZHOU UNIVERSITY   (China)

^b Liao Ning Shihua University   (China)

^c NORTHEASTERN UNIVERSITY   (China)

Author keywords

Damage detection; Data fusion; Feature extraction; Hybrid System; Modal data; Probabilistic neural network

Indexed keywords

DATA FUSION; FEATURE EXTRACTION; HYBRID COMPUTERS; HYBRID SYSTEMS; INFORMATION FUSION; MODAL ANALYSIS; NEURAL NETWORKS; SENSORS; SPEECH RECOGNITION; STRUCTURAL HEALTH MONITORING;

CLASSIFIER MODELS; COMPLEX STRUCTURE; CONFIDENCE LEVELS; DAMAGE DETECTION AND IDENTIFICATION; DATA FUSION TECHNIQUE; DATA-FUSION SYSTEMS; DECISION LEVEL FUSION; FEATURE LEVEL; FUSION MODEL; MEASURED DATA; MODAL DATA; MULTI SENSOR; NOISE LEVELS; NUMERICAL SIMULATION; PROBABILISTIC NEURAL NETWORKS; STEEL FRAME; STRUCTURAL HEALTH MONITORING SYSTEMS; THREE MODELS;

DAMAGE DETECTION;

EID: 79957784176     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2011.03.002     Document Type: Article

References (26)

1. H.W. Shih, DP. Thambiratnam, and T.H.T. Chan Vibration based structural damage detection in flexural members using multi-criteria approach J Sound Vib 323 2009 645 661
2. Renos.A. Votsis, and K. Marios Chryssanthopoulos. Assessment of debonding in GFRP joints using damage identification techniques Constr Build Mater 23 2009 1690 1697
3. Banerjee Sauvik, Ricci Fabrizio, Monaco Ernesto, and Mal Ajit A wave propagation and vibration-based approach for damage identification in structural components J Sound Vib 322 2009 167 183
4. E.P. Carden, and P. Fanning Vibration based condition monitoring: a review Struct Health Monit 3 4 2004 0355 377
5. A. Tomaszewska Influence of statistical errors on damage detection based on structural flexibility and mode shape curvature Comput Struct 88 2010 154 164
6. Huajun Li, Junrong Wang, and James Hu Sau-Lon Using incomplete modal data for damage detection in offshore jacket structures Ocean Eng 35 2008 1793 1799
7. D.L. Hall, and J. Llinas Handbook of multisensor data fusion 2001 CRC Press Boca Raton, FL
8. Y. Bar-Shalom, and W.D. Blair Multitarget-multisensor tracking: applications and advances vols. 3-2 2000 Artech House Boston
9. A.H. Gunatilaka, and B.A. Baertlein Feature-level and decision-level fusion of noncoincidently sampled sensors for land mine detection IEEE Trans Pattern Anal Mach Intell 23 6 2001 577 589 (Pubitemid 32585271)
10. D.L. Hall, and S.A.H. McMullen Mathematical techniques in multisensor data fusion 2004 Artech House Boston
11. H. Li, Y. Bao, and J. Ou Structural damage identification based on integration of information fusion and shannon entropy Mech Syst Signal Process 22 2008 1427 1440
12. X.F. Liu, L. Ma, and J. Mathew Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques Mech Syst Signal Process 23 2009 690 700
13. Shao-Fei Jiang, Chunming Zhang, and C.G. Koh Structural damage detection by integrating data fusion and probabilistic neural network Adv Struct Eng - Int J 9 4 2006 445 458
14. Shao-Fei Jiang, Chun-Ming Zhang, and Shuai Zhang Two-stage structural damage detection using fuzzy neural networks and data fusion techniques Expert Syst Appl 38 1 2011 511 519
15. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas On combining classifiers IEEE Trans Pattern Anal Mach Intell 20 1998 226 239 (Pubitemid 128741312)
16. Rahman A, Fairhurst M. Decision combination of multiple classifiers for pattern classification: hybridization of majority voting and divide and conquer techniques. In: Proceedings of the fifth IEEE conference on application of computer vision, Palm Springs, USA, CA; 2000. p. 58-63.
17. L.I. Kuncheva A theoretical study on six classifier fusion strategies IEEE Trans Pattern Anal Mach Intell 24 2002 281 286 (Pubitemid 34198215)
18. G. Fumera, and F. Roli A theoretical and experimental analysis of linear combiners for multiple classifier systems IEEE Trans Pattern Anal Mach Intell 27 2005 942 956 (Pubitemid 40889844)
19. C. Dimitrakakis, and S. Bengio Online adaptive polices for ensemble classifier Neurocomputing 64 2005 211 221 (Pubitemid 40425319)
20. Z.Q. Zhao, D.S. Huang, and B.Y. Sun Human face recognition based on multi-features using neural networks committee Pattern Recogn Lett 25 2004 1351 1358
21. Ö. Toygar, and A. Acan Multiple classifier implementation of a divide-and-conquer approach using appearance-based statistical methods for face recognition Pattern Recogn Lett 25 2004 1421 1430
22. D.J. Mashao, and M. Skosan Combining classifier decisions for robust speaker identification Pattern Recogn 39 2006 147 155 (Pubitemid 41525575)
23. X.C. Yin, C.P. Lin, and Z. Han Feature combination using boosting Pattern Recogn Lett 26 2005 2195 2205 (Pubitemid 41383619)
24. D.F. Specht Probabilistic neural networks Int J Neural Networks 3 1990 109 118
25. W.T. Thomson, and M.D. Dahleh Theory of vibration with applications 2005 Tsinghua University Press Beijing
26. F. Öcan, C.D. Atis, O. Karahan, E. Uncuolu, and H. Tanyildizi Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete Adv Eng Softw 40 2009 856 863