Revelence of Multiple Breathing Cracks on Fixed Shaft Using ANFIS and ANN

  • J. NandaEmail author
  • L. D. Das
  • S. Choudhury
  • D. R. Parhi
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The article is focused on the revelation of using AI technique to detect cracks in shaft using ANFIS, ANN based on error percentage comparing with experimental work. The effectiveness of crosswise loaded fixed-fixed shaft with multiple cracks is contemplated using theoretical and experimental analysis in the article. The presence of fractures with its positions and dimensions on vibration domain is identified considering the consequence of curtailment in stiffness. The fundamental frequencies including their mode patterns in varying positions and intensities are estimated. These outcomes received from the analytical model have applied by ANFIS and ANN utilizing the modal frame. The parameters so as first three fundamental frequencies including their mode shapes for several positions and intensities of the shaft are rendered to ANFIS and ANN network separately. The test model including the sustainable error checks the authenticity of the AI techniques (ANFIS and ANN design). It is concluded that the rate of average error in ANFIS and ANN based on the experimental investigation is 2.17% and 3.5%, respectively. The existing approach is simple for preparing a condition-monitoring model of the shaft applying in a structure.


Crack detection Vibration responses Multi cracks Shaft ANFIS and ANN 


  1. 1.
    Yu L, Cheng L, Yam LH, Yan YJ, Jiang JS (2009) Experimental validation of vibration-based damage detection for static laminated composite shells partially filled with fluid. Compos Struct 79(2):288–299CrossRefGoogle Scholar
  2. 2.
    Pawar PM, Reddy KV, Ganguli R (2007) Damage detection in beams using spatial fourier analysis and neural networks. J Intell Mater Syst Struct 18:347–360CrossRefGoogle Scholar
  3. 3.
    Hossain MA, Madkour AAM, Dahal KP, Yu H (2008) Comparative performance of intelligent algorithms for system identification and control. J Intell Syst 17:313–329Google Scholar
  4. 4.
    Saridakis KM, Chasalevris AC, Papadopoulos CA, Dentsoras AJ (2008) Applying neural networks, genetic algorithms and fuzzy logic for the identification of cracks in shafts by using coupled response measurements. Comput Struct 86(11–12):1318–1338CrossRefGoogle Scholar
  5. 5.
    Taghi M, Baghmisheh V (2008) Crack detection in beam-like structures using genetic algorithms. Appl Soft Comput 8(2):1150–1160CrossRefGoogle Scholar
  6. 6.
    Rafiee J, Tse PW, Harifi A, Sadeghi MH (2009) A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system. Expert Syst Appl 36(3):4862–4875CrossRefGoogle Scholar
  7. 7.
    Chandrashekhar M, Ganguli R (2009) Uncertainty handling in structural damage detection using fuzzy logic and Probabilistic simulation. Mech Syst Signal Process 23(2):384–404CrossRefGoogle Scholar
  8. 8.
    Panigrahi SK, Chakraverty S, Mishra BK (2009) Vibration based damage detection in a uniform strength beam using genetic algorithm. Meccanica 44:697–710MathSciNetCrossRefGoogle Scholar
  9. 9.
    Haryanto I, Setiawan JD, Budiyono A (2009) Structural damage detection using randomized trained neural networks. Stud Comput Intell 192:245–255Google Scholar
  10. 10.
    Lekhy D, Novak D (2009) Neural network based damage detection of dynamically loaded structures. Commun Comput Inf Sci 43:17–27zbMATHGoogle Scholar
  11. 11.
    Rubio L, Muñoz Abella B, Loaiza G (2011) Static behavior of a shaft with an elliptical crack. Mech Syst Signal Process 25:1674–1686CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • J. Nanda
    • 1
    Email author
  • L. D. Das
    • 2
  • S. Choudhury
    • 1
  • D. R. Parhi
    • 3
  1. 1.ITER, S’O’A UniversityBhubaneswarIndia
  2. 2.Department of Mechanical EngineeringVSSUT BurlaBurlaIndia
  3. 3.Department of Mechanical EngineeringN.I.T. RourkelaRourkelaIndia

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