Skip to main content
SpringerLink
Log in

Dimensional description of on-line wear debris images for wear characterization

  • Published:
Chinese Journal of Mechanical Engineering Submit manuscript

Abstract

As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle’s chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area (IPCA), show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. LIU Jin, WANG Jingqiu, WANG Chao. The technical research of ferrography division of morphology[J]. Advances in Intelligent and Soft Computing, 2012, 114: 1027–1037.

    Article  Google Scholar 

  2. ISA M C, YUSOFF N H N, NAIN H, et al. Ferrographic analysis of wear particles of various machinery systems of a commercial marine ship[J]. Procedia Engineering, 2013, 68: 345–351.

    Article  Google Scholar 

  3. MACIAN V, PAYRI R, TORMOS B, et al. Applying analytical ferrography as a technique to detect failures in diesel engine fuel injection systems[J]. Wear, 2006, 260(4–5): 562–566.

    Article  Google Scholar 

  4. MYSHKIN N K, MARKOVA L V, SEMENYUK M S, et al. Wear monitoring based on the analysis of lubricant contamination by optical ferroanalyzer[J]. Wear, 2003, 25(7–12): 1270–1275.

    Article  Google Scholar 

  5. XIAO Hanliang. The development of ferrography in China-some personal reflections[J]. Tribology International, 2005, 38(10): 904–907.

    Article  Google Scholar 

  6. ROYLANCE B J. Ferrography-then and now[J]. Tribology International, 2005, 38(10): 857–862.

    Article  Google Scholar 

  7. LIU Yan, LIU Zhong, XIE Youbai, et al. Research on an on-line wear condition monitoring system for marine diesel engine[J]. Tribology International, 2000, 33(12): 829–835.

    Article  Google Scholar 

  8. WU Tonghai, MAO Junhong, WANG Jingtao, et al. A new on-line visual ferrograph[J]. Tribology Transaction, 2009, 52(5): 623–631.

    Article  Google Scholar 

  9. WU Tonghai, QIU Huipeng, WU Jiaoyi, et al. Image digital processing technology for visual on-line ferrograph sensor[J]. Chinese Journal of Mechanical Engineering, 2008, 44(9): 83–87. (in Chinese)

    Article  Google Scholar 

  10. WU Tonghai, WANG Junqun, WU Jiaoyi, et al. Wear characterization by an on-line ferrograph image[J]. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2011, 225(1): 23–34.

    Google Scholar 

  11. CAO Yibo, XIE Xiaopeng. Wear particles classification based on wavelet transform and back-propagation neural network[C]//Computational Engineering in Systems Applications, IMACS Multiconference on. IEEE, 2006, 2: 2010–2014.

    Google Scholar 

  12. RAADNUI S. Wear particle analysis-utilization of quantitative computer image analysis: a review[J]. Tribology International, 2005, 38(10): 871–878.

    Article  Google Scholar 

  13. PODSIADLO P, STACHOWIAK G W. Development of advanced quantitative analysis methods for wear particle characterization and classification to aid tribological system diagnosis[J]. Tribology International, 2005, 38(10): 887–897.

    Article  Google Scholar 

  14. WU Tonghai, WANG Junqun, PENG Yeping, et al. Description of wear debris from online ferrograph images by their statistical color. Tribol[J]. Tribology Transactions, 2012, 55(5): 606–614.

    Article  Google Scholar 

  15. WU Hongkun, WU Tonghai, PENG Yeping, et al. Watershed-based morphological separation of wear debris chains for on-line ferrograph analysis[J]. Tribology Letters, 2014, 53(2): 411–420.

    Article  Google Scholar 

  16. ZHANG Yali, MAO Junhong, XIE Youbai. Engine wear monitoring with OLVF[J]. Tribology Transactions, 2011, 54(2): 201–207.

    Article  Google Scholar 

  17. WU Jiaoyi, MI Xinyan, WU Tonghai. A wavelet-analysis-based differential method for engine wear monitoring via on-line visual ferrograph[J]. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2013, 227(12): 1356–1366.

    Article  Google Scholar 

  18. YU Shiqiang, DAI Xingjian. Wear particle image segmentation method based on the recognition of background color[J]. Tribology, 2007, 27(5): 467–471. (in Chinese)

    Google Scholar 

  19. WU Tonghai, WANG Weigang, WU Jiaoyi, et al. Improvement on on-line ferrograph image identification[J]. Chinese Journal of Mechanical Engineering, 2010, 23(1): 1–6.

    Article  MATH  Google Scholar 

  20. LI Xianbin, LI Houqiang, WANG Fuquan, et al. A remark on the mallat pyramidal algorithm of wavelet analysis[J]. Communication in Nonlinear Science & Numerical Simulation, 1997, 2(4): 240–243.

    Article  Google Scholar 

  21. MURTAGH F, STARCK J L. Wavelet and curvelet moments for image classification: Application to aggregate mixture grading[J]. Pattern Recognition Letters, 2008, 29(10): 1557–1564.

    Article  Google Scholar 

  22. MRINAL K B, DEBOTOSH B, MITA N, et al. Quotient based multiresolution image fusion of thermal and visual images using daubechies wavelet transform for human face recognition[J]. International Journal of Computer Science Issues, 2010, 7(3): 18–22.

    Google Scholar 

  23. ZHANG G, SCHLARB A K. Morphologies of the wear debris of polyetheretherketone produced under dry sliding conditions: correlation with wear mechanisms[J]. Wear, 2009, 266(7–8): 745–752.

    Article  Google Scholar 

  24. KUMAR M, MUKHERJEE P S, MISRA N M. Advancement and current status of wear debris analysis for machine condition monitoring: a review[J]. Industrial Lubrication and Tribology, 2013, 65(1): 3–11.

    Article  Google Scholar 

  25. GONCALVES V D, ALMEIDA L F, HUGO M. Wear particle classifier system based on an artificial neural network[J]. Journal of Mechanical Engineering, 2010, 56(4): 277–281.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Modern Design and Rotor Bearing System of Ministry, Xi’an Jiaotong University, Xi’an, 710049, China

    Tonghai Wu, Yeping Peng, Ying Du & Junqun Wang

  2. School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, 2052, Australia

    Tonghai Wu

Authors
  1. Tonghai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeping Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junqun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tonghai Wu.

Additional information

Supported by the National Natural Science Foundation of China (Grant Nos. 51275381, 50905135), Shaanxi Provincial Science and Technology Planning Project of China (Grant No. 2012GY2-37)

WU Tonghai, born in 1976, is currently an associate professor at Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, China. He obtained his PhD degree from Xi’an Jiaotong University, China, in 2006. His research interests include tribology system and intelligent monitoring.

PENG Yeping, born in 1988, is currently a PhD candidate at Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, China. Her research interests include tribology system and intelligent monitoring.

DU Ying, born in 1989, is currently a PhD candidate at Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, China. Her research interests include tribology system and intelligent monitoring.

WANG Junqun, born in 1985, is currently a MD candidate at Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, China. His research interests include tribology system and intelligent monitoring.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, T., Peng, Y., Du, Y. et al. Dimensional description of on-line wear debris images for wear characterization. Chin. J. Mech. Eng. 27, 1280–1286 (2014). https://doi.org/10.3901/CJME.2014.0808.132

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3901/CJME.2014.0808.132

Keywords

Search

Navigation