Wear Particle Analysis Using Fractal Techniques

  • Puja P. MoreEmail author
  • M. D. Jaybhaye
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Wear particle characterization plays a important role in condition monitoring of machine as most of the breakdown occurs due to wear particle saturation in the lubricating oil. Traditional methods for wear debris analysis depend on human expertise to conclude the results, which are subjective in nature, time consuming, and costly. The objective of this paper is to categorize different techniques of fractal analysis to study the wear particle morphology and calculate fractal dimension of wear particles. Fractal analysis is used to give information about different features of wear particles like fractal dimension, shape, size, color, boundary representation, and surface/texture analysis. This data can be used to detect the fault and decide prognostic maintenance period.


Wear particle Fractal analysis Fractal dimension Condition monitoring ImageJ 


  1. 1.
    Kumar M (2013) Advancement and current status of wear debris analysis for machine condition monitoring: a review. Ind Lubr Tribol 65:3–11CrossRefGoogle Scholar
  2. 2.
    Stachowiak GW (1998) Numerical characterization of wear particles morphology and angularity of particles and surfaces. Tribol Int 31:139–157CrossRefGoogle Scholar
  3. 3.
    Kumar C Kumar M (2016) Wear debris analysis using ferrography. Int J Recent Trends Eng Res 2(8):398–404Google Scholar
  4. 4.
    Podsiadlo P, Podsiadlo GW (2000) Scale-invariant analysis of wear particle surface morphology: II. Fractal Dimension Wear 242:180–188Google Scholar
  5. 5.
    Raadnui S (2005) Wear particle analysis—utilization of quantitative computer image analysis: a review. Tribol Int 38(10):871–878CrossRefGoogle Scholar
  6. 6.
    Kirk TB, Panzera D, Anamalay RV, Xu ZL (1995) Computer image analysis of wear debris for machine condition monitoring and fault diagnosis. Wear 181:717–722CrossRefGoogle Scholar
  7. 7.
    Ghosh S, Sarkar B (2005) Wear characterization by fractal mathematics for quality improvement of machine. J Q Maintenance Eng 11(4):318–332Google Scholar
  8. 8.
    Lopes R, Betrouni N (2009) Fractal and multifractal analysis: a review. Med Image Anal 13:634–649CrossRefGoogle Scholar
  9. 9.
    Debnath L (2006) A brief historical introduction to fractals and fractal geometry. Int J Math Educ Sci Technol 37:29–50Google Scholar
  10. 10.
    Kang MC, Kim JS, Kim KH (2005) Fractal dimension analysis of machined surface depending on coated tool wear. Surf Coat Technol 193(1–3):259–265CrossRefGoogle Scholar
  11. 11.
    Shah H, Hirani H (2014) Online condition monitoring of spur gears. Int J Condition Monit 4:1–8Google Scholar
  12. 12.
    Kirk TB, Stachowiak GW, Batchelor AW (1991) Fractal Parameters and computer image analysis applied to wear particles isolated by ferrography. Wear 145:347–365CrossRefGoogle Scholar
  13. 13.
    Stachowiak GW, Kirk TB, Stachowiak GB (1991) Ferrography and fractal analysis of contamination particles in unused lubricating oils. Tribol Int 6:329–334CrossRefGoogle Scholar
  14. 14.
    So GB, So HR, Jin GG (2017) Enhancement of the box-counting algorithm for fractal dimension estimation. Pattern Recognit Lett 98:53–58CrossRefGoogle Scholar
  15. 15.
    Li J, Du Q, Sun C (2009) An improved box-counting method for image fractal dimension estimation. Pattern Recogn 42(11):2460–2469CrossRefGoogle Scholar
  16. 16.
    Gonzato G, Mulargia F, Marzocchi W (1998) Practical application of fractal analysis: problems and solutions. The Charlesworth Group 132:275–282zbMATHGoogle Scholar
  17. 17.
    Hong-tao L, Shi-rong G (2009) Fishbone graph fractal description to UHMWPE wear debris boundary. Tribol Int 42(11–12):1624–1628CrossRefGoogle Scholar
  18. 18.
    Shirong G, Guoan C, Xiaoyun Z (2001) Fractal characterization of wear particle accumulation in the wear process. Wear 251(1–12):1227–1233CrossRefGoogle Scholar
  19. 19.
    Klinkenberg B (1994) A review of methods used to determine the fractal dimension of linear features. Math Geol 26(1):23–46CrossRefGoogle Scholar
  20. 20.
    Karperien A (2004) FracLac advanced user manual. Charles Sturt University, AustraliaGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Department of Production Engineering and Industrial ManagementCollege of Engineering PunePuneIndia

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