Damage detection in structural beam elements using hybrid neuro fuzzy systems

다국어 초록 (Multilingual Abstract)

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures

참고문헌 (Reference)

1 Dackermann, U., "Vibration-based damage identification methods for civil engineering structures using artificial neural networks" University of Technology 2010

2 Fan, W, "Vibration-based damage identification methods : A review and comparative study" 10 (10): 83-111, 2011

3 Sahin, M., "Vibration-based damage identification in beam-like composite laminates by using artificial neural networks" 217 (217): 661-676, 2003

4 Oruganti, K., "Vibration-based analysis of damage in composites" 33 : 496-504, 2009

5 Barai, S.V., "Vibration signature analysis using artificial neural networks" 9 (9): 259-265, 1995

6 Dimarogonas, A. D., "Vibration of cracked structures : A state of the art review" 55 : 831-857, 1996

7 Ni, Y. Q., "Vibration based damage localization in Ting Kau Bridge using probabilistic neural network" 2 : 1069-1076, 2000

8 Carden, P.E., "Vibration based condition monitoring : A review" 3 (3): 355-377, 2004

9 Weaver, W., "Vibration Problems in Engineering" Wiley 1990

10 Bharadwaj Nanda, "Vibration Based Structural Damage Detection Technique using Particle Swarm Optimization with Incremental Swarm Size" 한국항공우주학회 13 (13): 323-331, 2012

11 Wu, X., "Use of neural networks in detection of structural damage" 42 : 649-659, 1992

12 Saeed, R.A, "The use of ANN for cracks predictions in curvilinear beams based on their natural frequencies and frequency response functions" 3 (3): 113-125, 2011

13 Saadat, S. A., "Structural system identification and damage detection using the intelligent parameter varying technique : An experimental study" 6 (6): 231-243, 2007

14 Fang, X., "Structural damage detection using neural network with learning rate improvement" 83 (83): 2150-2161, 2005

15 Zang, C., "Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection" 242 (242): 813-827, 2001

16 Chang, C. C., "Structural damage detection using an iterative neural network" 11 : 32-42, 2000

17 Tsou, P, "Structural damage detection and identification using neural networks" 32 (32): 176-183, 1994

18 Sawyer, J., "Structural damage detection and identification using fuzzy logic" 38 : 2328-2335, 2000

19 Bakhary, N., "Structural condition monitoring and damage identification with artificial neural network" University of Western Australia 2008

20 Pawar, P.M., "Structural Health Monitoring Using Genetic Fuzzy Systems" Springer-Verlag 2011

21 Sinha, J. K., "Simplified models for the location of cracks in beam structures using measured vibration data" 251 (251): 13-38, 2002

22 Gonzalez, M.P., "Seismic damage identification in buildings using neural networks and modal data" 86 (86): 416-426, 2008

23 Wang, J.H, "Reducing errors in the identification of structural joint parameters using error functions" 273 (273): 295-316, 2004

24 Wei, M., "Predicting injection profiles using ANFIS" 177 : 4445-4461, 2007

25 Zio, E., "Neuro-fuzzy pattern classification for fault diagnosis" 33 : 415-426, 2006

26 Karunanithi, N., "Neural networks for river flow prediction" 8 (8): 201-220, 1994

27 Nyongesa, H. O., "Neural fuzzy analysis of delaminated composites from shearography imaging" 54 (54): 313-318, 2001

28 Haykin, S., "Neural Networks – A Comprehensive Foundation" Prentice-Hall 1998

29 Jang, J. S. R., "Neoro-Fuzzy and Soft Computing" Prentice Hall 1997

30 Vinayak, H. K., "NN based damage detection from modal parameter changes" 2008

31 Caddemi, S., "Multi-cracked Euler–Bernoulli beams : Mathematical modeling and exact solutions" 50 : 944-956, 2013

32 Marseguerra, M., "Model identification by neuro-fuzzy techniques : predicting the water level in a steam generator of a PWR" 44 (44): 237-252, 2004

33 Li, H., "Modal strain energy decomposition method for damage localization in 3D frame structures" 132 (132): 941-951, 2006

34 Ye, Z., "Mechanical fault diagnostics for induction motor with variable speed drives using adaptive neuro-fuzzy inference system" 76 : 742-752, 2006

35 Pawar, P.M., "Matrix cracking detection in thin-walled composite beam using genetic fuzzy system" 16 (16): 381-468, 2005

36 Su, Z., "Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm" 66 (66): 627-637, 2004

37 Abonyi, J., "Inverse fuzzy process model based direct adaptive control" 51 (51): 119-132, 1999

38 Dempsey, P., "Integrated oil debris and vibration gear damage detection technologies using fuzzy logic" 49 : 109-116, 2004

39 Chiu, S., "Fuzzy model identification based on cluster estimation" 2 : 267-278, 1994

40 Zadeh, L. A., "Fuzzy logic = computing with words" 4 (4): 103-111, 1996

41 Altug, S., "Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis" 46 (46): 1069-1079, 1999

42 Czogaa, E., "Fuzzy and Neuro-Fuzzy Intelligent Systems" Springer-Verlag 2000

43 Tran, V. T., "Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference" 36 (36): 1840-1849, 2009

44 Du, J., "Fault diagnosis in air-handling unit system using dynamic fuzzy neural network" 2004

45 Chen, J., "Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems" 16 (16): 585-596, 2008

46 Garesci, F., "Experimental results of a damage detection methodology using variations in modal parameters" 46 : 441-451, 2006

47 Hamey, C. S., "Experimental damage identification of carbon/epoxy composite beams using curvature mode shapes" 3 (3): 333-353, 2004

48 Cobaner, M., "Evapotranspiration estimation by two different neuro-fuzzy inference systems" 398 (398): 292-302, 2011

49 Yager, R.R., "Essentials of Fuzzy Modeling and Control" 1994

50 Yan, Y., "Development in vibration-based structural damage detection technique" 21 : 2198-2211, 2007

51 Babu, T.R., "Detection of two cracks in a rotor-bearing system using amplitude deviation curve" 314 (314): 457-464, 2008

52 Shi, Z. Y., "Damage localization by directly using incomplete mode shapes" 126 (126): 656-660, 2000

53 Lauwagie, T., "Damage identification in beams using inverse methods" 2002

54 Kim, B. H., "Damage estimation on beam-like structures using the multi-resolution analysis" 423 (423): 4238-4257, 2006

55 Zapico, J. L., "Damage assessment using neural network" 17 (17): 119-125, 2003

56 Ramu, S., "Damage assessment of composite structures using fuzzy logic integrated neural-network approach" 57 : 491-502, 1995

57 Kisi, O., "Daily pan evaporation modeling using a neuro-fuzzy computing technique" 329 (329): 636-646, 2006

58 Friswell, M.I., "Crack modeling for structural health monitoring" 1 (1): 139-148, 2002

59 Saeed, R. A., "Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions" 21 (21): 1629-1645, 2012

60 Mun-BoShim, "Crack Identification Using Neuro-Fuzzy-Evolutionary Technique" 대한기계학회 16 (16): 4-467, 2002

61 Drake, J. T., "Communications phase synchronization using the adaptive network fuzzy inference system" New Mexico State University 2000

62 Zang, C, "Combined neural network and reduced FRF techniques for slight damage detection using measured response data" 71 (71): 525-536, 2001

63 Soh, C., "Calibration of piezo-impedance transducers for strength prediction and damage assessment of concrete" 14 : 671-684, 2005

64 Li, Q. S., "Buckling of multi-step cracked columns with shear deformation" 23 : 356-364, 2001

65 Vieira, J., "Artificial neural networks and neuro-fuzzy systems for modeling and controlling real systems : a comparative study" 17 : 265-273, 2004

66 Choi, F. C., "Application of the modified damage index method to timber beams" 30 (30): 1124-1145, 2008

67 Thatoi, D. N., "Application of artificial intelligence techniques for detection of cracks-A review" 5 (5): 57-59, 2013

68 Aydin, K, "Applicability of fuzzy genetic system for crack diagnosis in Timoshenko beams" 29 (29): 2015

69 Jang, J. S. R., "ANFIS : adaptive-network-based fuzzy inference system" 23 (23): 665-685, 1993

70 Sohn, H., "A review of structural health monitoring literature: 1996-2001" Los Alamos National Laboratory 2004

71 Lei, Y., "A new approach to intelligent fault diagnosis of rotating machinery" 35 (35): 1593-1600, 2008

72 Zio, E., "A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery" 94 : 78-88, 2009

73 Kao, C.Y, "A neural network-based approach for detection of structural damage" 2005

74 Zhao, Z., "A fuzzy system for concrete bridge damage diagnosis" 80 : 629-641, 2002

75 Ganguli, R., "A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data" 12 (12): 397-407, 2001