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Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions

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Abstract

This paper presents different artificial intelligence (AI) techniques for crack identification in curvilinear beams based on changes in vibration characteristics. Vibration analysis has been performed by applying the finite element method (FEM) to compute natural frequencies and frequency response functions (FRFs) for intact and damaged beams. The analysis reveals the changes in natural frequencies and amplitudes of FRFs of the beams for cracks of different sizes at different locations. These changes are used as input data for single and multiple artificial neural networks (ANN) and multiple adaptive neuro-fuzzy inference systems (ANFIS) in order to predict the size of the crack and its location. To avoid large models, the principal component analysis (PCA) approach has been carried out to reduce the computed FRFs data. The analysis of different techniques shows that the average prediction errors in the multiple ANN models is less than those in the single ANN model and in the multiple ANFIS. It is shown that the cracks longer than 5 mm can be located with satisfactory accuracy, even if the input data are corrupted with various level of noise. Multiple ANFIS is adopted to construct a more reliable and less sensitive model for noise excitation.

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References

  1. Ahmad Z, Zhang J (2005) Bayesian selective combination of multiple neural networks for improving long range predictions in nonlinear process modelling. Neural Comput Appl 14(1):78–87

    Article  Google Scholar 

  2. Altug S, Chen MY, Trussell HJ (1999) Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis. IEEE Trans Ind Electron 46(6):1069–1079

    Article  Google Scholar 

  3. Angelakis C, Loukis EN, Pouliezos AD, Stavrakakis GS (2001) Neural network based method for gas turbine blading fault diagnosis. Int J Model Simul 21(1):51–60

    Google Scholar 

  4. Bamnios G, Trochides A (1995) Dynamic behaviour of a cracked cantilever beam. Appl Acoust 45(2):97–112

    Article  Google Scholar 

  5. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    Google Scholar 

  6. Buonsanti M, Cacciola M, Calcagno S, Morabito FC, Versaci M (2007) Fuzzy computation for classifying defects in metallic plates. Int J Appl Electromagnet Mech 25:325–331

    Google Scholar 

  7. Chen J, Roberts C, Weston P (2008) Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems. Control Eng Pract 16(5):585–596

    Article  Google Scholar 

  8. Choubey A, Sehgal DK, Tandon N (2006) Finite element analysis of vessels to study changes in natural frequencies due to cracks. Int J Press Vessels Piping 83(3):181–187

    Article  Google Scholar 

  9. Daoming G, Chen J (2006) ANFIS for high-pressure water jet cleaning prediction. Surf Coat Technol 201(3–4):1629–1634

    Article  Google Scholar 

  10. Goldman S (1999) Vibration spectrum analysis. Industrial Press, New York

    Google Scholar 

  11. Hu Y, Hwang JN (2002) Handbook of neural network signal processing. CRC Press, Boca Raton, FL

    Google Scholar 

  12. Huynh D, He J, Tran D (2005) Damage location vector: a non-destructive structural damage detection technique. Comput Struct 83(28–30):2353–2367

    Article  Google Scholar 

  13. Ivancevic V, Ivancevic T (2007) Neuro-fuzzy associative machinery for comprehensive brain and cognition modelling. Springer, Berlin

    Book  MATH  Google Scholar 

  14. Jang J-SR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Upper Saddle River, NJ

    Google Scholar 

  15. Jolliffe IT (1986) Principal component analysis. Springer, New York

    Google Scholar 

  16. Júnior VL, Turra AE (1996) Diagnosis of rotating systems using artificial neural networks. Transactions on information and communications technologies. WIT Press, Ashurst

    Google Scholar 

  17. Karthikeyan M, Tiwari R, Talukdar S (2007) Crack localisation and sizing in a beam based on the free and forced response measurements. J Mech Syst Signal Process 21(3):1362–1385

    Article  Google Scholar 

  18. Kim JT, Stubbs N (2003) Crack detection in beam-type structures using frequency data. J Sound Vib 259(1):145–160

    Article  Google Scholar 

  19. Kuo RJ (1995) Intelligent diagnosis for turbine blade faults using artificial neural network and fuzzy logic. Eng Appl Artif Intell 8:25–34

    Article  Google Scholar 

  20. Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech Syst Signal Process 21(5):2280–2294

    Article  Google Scholar 

  21. Liu SW, Huang JH, Sung JC, Lee CC (2002) Detection of cracks using neural networks and computational mechanics. Comput Methods Appl Mech Eng 191(25):2831–2845

    Article  MATH  Google Scholar 

  22. Maclntyre J, Tait J, Kendal S, Smith P, Harris T, Brason A (1994) Neural networks applications in condition monitoring. Applications of artificial intelligence in engineering. WIT press, Ashurst

    Google Scholar 

  23. Marwala T, Hunt H (1999) Fault identification using finite element models and neural networks. Mech Syst Signal Process 13(3):475–490

    Article  Google Scholar 

  24. Orhan S (2007) Analysis of free and forced vibration of a cracked cantilever beam. J Sound Vib 40(6):443–450

    MathSciNet  Google Scholar 

  25. Owolabi GM, Swamidas ASJ, Seshadri R (2003) Crack detection in beams using changes in frequencies and amplitudes of frequency response functions. J Sound Vib 265(1):1–22

    Article  Google Scholar 

  26. Patil DP, Maiti SK (2003) Detection of multiple cracks using frequency measurements. Eng Fract Mech 40:1553–1572

    Article  Google Scholar 

  27. Rizos PF, Aspragathos N, Dimarogonas AD (1990) Identification of crack location and magnitude in a cantilever beam from the vibration modes. J Sound Vib 138(3):381–388

    Article  Google Scholar 

  28. Saeed AS, Galybin AN (2009) Simplified model of the turbine runner blade. Eng Fail Anal 16(7):2473–2484

    Article  Google Scholar 

  29. Saeed RA, Galybin AN, Popov V, Abdulrahim NO (2009a) Modelling of Francis turbine runner of power station: part I—flow simulation study. In: Proceedings of the fluid-structure interaction, WIT press, Crete, Greece

  30. Saeed RA, Galybin AN, Popov V, Abdulrahim NO (2009b) Modelling of Francis turbine runner of power station: part II—stress analysis. In: Proceedings of the fluid-structure interaction. WIT press, Crete, Greece

  31. Salawu OS (1997) Detection of structural damage through changes in frequency: a review. Eng Struct 19(9):718–723

    Article  Google Scholar 

  32. Sridhar DV, Bartlett EB, Seagrave RC (1996) An information theoretic approach for combining neural network process model. Neural Netw 12:915–926

    Article  Google Scholar 

  33. Tran VT, Yang BS, Oh MS, Tan ACC (2009) Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Syst Appl 36(2):1840–1849

    Article  Google Scholar 

  34. Vassilopoulos AP, Bedi R (2008) Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates. Comput Mater Sci 43(4):1086–1093

    Article  Google Scholar 

  35. Yang XF, Swamidas ASJ, Seshadri R (2001) Crack identification in vibrating beams using the energy method. J Sound Vib 244(2):339–357

    Article  Google Scholar 

  36. Ye Z, Sadeghian A, Wu B (2006) Mechanical fault diagnostics for induction motor with variable speed drives using adaptive neuro-fuzzy inference system. Electric Power Syst Res 76:742–752

    Article  Google Scholar 

  37. Yun CB, Bahng EY (2000) Substructural identification using neural networks. Comput Struct 77(1):41–52

    Article  Google Scholar 

  38. Zang C, Imregun M (2001) Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. J Sound Vib 242(5):813–827

    Article  Google Scholar 

  39. Zhang J, Martin EB, Morris AJ, Kiparissides C (1997) Inferential estimation of polymer quality using stacked neural networks. Comput Chem Eng 21:1025–1030

    Google Scholar 

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Authors and Affiliations

  1. Wessex Institute of Technology, Ashurst Lodge, Ashurst, Southampton, SO40 7AA, UK

    R. A. Saeed, A. N. Galybin & V. Popov

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  1. R. A. Saeed
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  2. A. N. Galybin
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  3. V. Popov
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Correspondence to R. A. Saeed.

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Saeed, R.A., Galybin, A.N. & Popov, V. Crack identification in curvilinear beams by using ANN and ANFIS based on natural frequencies and frequency response functions. Neural Comput & Applic 21, 1629–1645 (2012). https://doi.org/10.1007/s00521-011-0716-1

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  • DOI: https://doi.org/10.1007/s00521-011-0716-1

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