Reliability estimation of washing machine spider assembly via classification

  • Recep Gorguluarslan
  • Eui-Soo KimEmail author
  • Seung-Kyum Choi
  • Hae-Jin Choi


This paper presents a study on the reliability estimation of the spider assembly of the front loading washing machine. To achieve the analytical certification of the current design of the spider assembly of the washing machine, fatigue life test, finite element analysis, physical experimentation, and a classification processes were conducted. First, the conventional finite element analysis and fatigue life analysis were conducted and their simulation results have been validated by physical experiments in this research. The probability of failure is estimated by a classification process. Specifically, the probabilistic neural network classifier is incorporated into the simulation process to reduce the number of finite element analysis calculations while ensuring the prediction accuracy of the failure probability. Based on the estimated failure probability and other structural analysis results, the margin of the performance of the spider assembly is fully identified.


Probabilistic neural network Latin hypercube sampling Front loading washing machine Reliability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lim HT, Jeong WB, Kim KJ (2010) Dynamic modeling and analysis of drum-type washing machine. Int J Precis Eng Manuf 11(3):407–417CrossRefGoogle Scholar
  2. 2.
    Bae S, Lee JM, Kang YJ, Kang JS, Yun JR (2002) Dynamic analysis of an automatic washing machine with a hydraulic balancer. J Sound Vib 257(1):3–18CrossRefGoogle Scholar
  3. 3.
    Al-Bedoor BO (1999) A Dynamic model of coupled shaft torsional and blade bending deformations in rotors. Comput Methods Appl Mech Eng 169(1–2):177–190CrossRefGoogle Scholar
  4. 4.
    Khalid YA, Mutasher SA, Sahari BB, Hamouda AMS (2007) Bending fatigue behavior of hybrid aluminum/composite drive shafts. Mater & Des 28:329–334CrossRefGoogle Scholar
  5. 5.
    Savaidis A, Savaidis G, Zhang C (2001) FE fatigue analysis of notched elastic-plastic shaft under multiaxial loading consisting of constant and cyclic components. Int J Fatigue 23:303–315CrossRefGoogle Scholar
  6. 6.
    Juuma T (2000) Torsional fretting fatigue strength of a shrink-fitted shaft with a grooved hub. Tribol Int 33:537–543CrossRefGoogle Scholar
  7. 7.
    Patel J, Choi S-K (2012) Classification approach for reliability-based topology optimization using probabilistic neural networks. Struct Multidisc Opt 45:529–543MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257CrossRefGoogle Scholar
  9. 9.
    Brown DE, Corruble V, Pittard CL (1993) A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problems. Pattern Recog 26:953–961CrossRefGoogle Scholar
  10. 10.
    Curram SP, Mingers J (1994) Neural networks, decision tree induction and discriminant analysis: an emperical comparison. Eur J Oper Res Soc 45(4):440–450CrossRefGoogle Scholar
  11. 11.
    Patuwo E, Hu MY, Hung MS (1993) Two-group classification using neural networks. J Decis Sci 24(4):825–845CrossRefGoogle Scholar
  12. 12.
    Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263–286CrossRefGoogle Scholar
  13. 13.
    Duda PO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New YorkzbMATHGoogle Scholar
  14. 14.
    Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural, and statistical classification. Ellis Horwood, London, UKzbMATHGoogle Scholar
  15. 15.
    Richard MD, Lippmann R (1991) Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Comput 3:461–483CrossRefGoogle Scholar
  16. 16.
    Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109–118CrossRefGoogle Scholar
  17. 17.
    Deng J, Yue ZQ, Tham LG, Zhu HH (2003) Pillar design by combining finite element methods, neural networks, and reliability: a case study of the Feng Huangshan copper mine, China. Int J Rock Mech Min Sci 40:585–599CrossRefGoogle Scholar
  18. 18.
    Sinha SK, Pandey MD (2002) Probabilistic neural network for reliability assessment of oil and gas pipelines. Computer Aided Civil and Infrastucture Eng 17:320–329CrossRefGoogle Scholar
  19. 19.
    Tam CM, Thomas KLT, Tony CTL, Chan KK (2004) Diagnosis of prestressed concrete pile defects using probabilistic neural networks. Eng Struct 26:1155–1162CrossRefGoogle Scholar
  20. 20.
    Kim DK, Lee JJ, Lee JH, Chang SK (2005) Application of probabilistic neural networks for prediction of concrete strength. J Mater Civ Eng 17(3):353–362CrossRefGoogle Scholar
  21. 21.
    Tran DH, NG AWM, Perera BJC, Burn S, Davis P (2006) Application of probabilistic neural networks in modelling structural deterioration of stormwater pipes. Urban Water J 3(3):175–184CrossRefGoogle Scholar
  22. 22.
    Papadrakakis M, Papadopoulos V, Lagaros ND (1996) Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 136:145–163CrossRefGoogle Scholar
  23. 23.
    Elhewy AH, Mesbahi E, Pu Y (2006) Reliability analysis of structures using neural network method. Probabilistic Eng Mech 21:44–53CrossRefGoogle Scholar
  24. 24.
    Cardoso JB, Almeida JR, Dias JM, Coelho PG (2008) Structural reliability anlaysis using Monte Carlo simulation and neural networks. Adv Eng Softw 39:505–513CrossRefGoogle Scholar
  25. 25.
    Hurtado JE (2001) Neural networks in sthocastic mechanics. Archives of Computational Methods Eng 8(3):303–342CrossRefGoogle Scholar
  26. 26.
    Hurtado JE, Alvarez DA (2001) Neural-network based reliability analysis: a comperative study. Comput Methods Appl Mech Eng 191:113–132CrossRefGoogle Scholar
  27. 27.
    Xu K, Xie M, Tang LC, Ho SL (2003) Application of neural networks in forecasting engine systems reliability. Appl Soft Comput 2:255–268CrossRefGoogle Scholar
  28. 28.
    Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065–1076MathSciNetCrossRefGoogle Scholar
  29. 29.
    Choi S-K, Grandhi RV, Canfield RA (2006) Reliability-based structural design. Springer, LondonzbMATHGoogle Scholar
  30. 30.
    ANSYS Academic Research, Release 13.0, ANSYS IncGoogle Scholar
  31. 31.
    The Math Works, Inc. MATLAB R2011a, Natick, MA: The MathWorks. 1994.Google Scholar
  32. 32.
    Hurtado JE (2002) Analysis of one-dimensional stochastic finite elements using neural networks. Probabilistic Eng Mech 17:35–44CrossRefGoogle Scholar
  33. 33.
    Mason RL, Gunst RF, Hess JL (2003) Statistical design and analysis of experiments-with applications to engineering and science, 2nd edn. Wiley-Interscience, New YorkzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Recep Gorguluarslan
    • 1
  • Eui-Soo Kim
    • 2
    Email author
  • Seung-Kyum Choi
    • 1
  • Hae-Jin Choi
    • 3
  1. 1.G.W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.National Forensic ServiceSeoulSouth Korea
  3. 3.School of Mechanical EngineeringChung-Ang UniversitySeoulSouth Korea

Personalised recommendations