Skip to main content
SpringerLink
Log in

Intelligent identification of wear mechanism via on-line ferrograph images

  • Published:
Chinese Journal of Mechanical Engineering Submit manuscript

Abstract

Condition based maintenance(CBM) issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.

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. WANG Yonghong. The study of wear particle recognition based on ferrography[D]. Hangzhou: Zhejiang University, 2002.

    Google Scholar 

  2. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. 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 

  6. YUAN Chengqing, JIN Zhongmin, TIPPER J L, et al. Numerical surface characterization of wear debris from artificial joints using atomic force microscopy[J]. Chinese Science Bulletin, 2009, 54(24):4583–4588.

    Article  Google Scholar 

  7. WU Tonghai, WANG Weigan, WU Jiaoyi, et al. Improvement on on-line ferrograph image identification[J]. Chinese Journal of Mechanical Engineering, 2010, 23(1):1–5.

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. 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 

  12. KONG N S P, IBRAHIM H. Color image enhancement using brightness preserving dynamic histogram equalization[J]. IEEE Transactions on Consumer Electronics, 2008, 54(4):1962–1968.

    Article  Google Scholar 

  13. LIU Tonggang, TANG Xiaohuang, YANG Ziyi. The theory of debris group in ferrographic analysis[C]//Technical Sessions—Proceedings of CIST2008 & ITS-IFToMM2008, Beijing, China, 2009: 361–365.

    Google Scholar 

  14. YAM JYF, CHOW TWS. A weight initialization method for improving training speed in feedforward neural network[J]. Neurocomputing, 2000, 30(1–4):219–232.

    Article  Google Scholar 

  15. HUANG Chuanhui, ZHU Hua, GE Shirong. Study on relevance between contour fractal dimension of wear debris and wear state[J]. Tribology, 2003(4): 336–339. (in Chinese)

    Google Scholar 

  16. STACHOWIAK G W, PODSIADLO P. Surface characterization of wear particles[J]. Wear, 1999, 225–229(2):1171–1185.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing Systems, Xi’an Jiaotong University, Xi’an, 710049, China

    Tonghai Wu, Yeping Peng & Jiaoyi Wu

  2. Reliability Engineering Institute, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, 430063, China

    Chenxing Sheng

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. Chenxing Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiaoyi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tonghai Wu.

Additional information

This project is supported by National Natural Science Foundation of China(Grant Nos. 50905135, 51275381)

WU Tonghai, born in 1976, is currently an associate professor at Theory of Lubrication and Beating Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Beating Systems, 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 master candidate at Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing Systems, Xi’an Jiaotong University, China. His research interests include tribology system and intelligent monitoring.

SHENG Chenxing, born in 1969, is currently an associate professor at Reliability Engineering Institute, School of Energy and Power Engineering, Key Laboratory of Marine Power Engineering and Technology, Wuhan University of Technology, China. He obtained his PhD degree from Wuhan University of Technology, China, in 2009. His research interests include machinery condition monitoring, fault diagnosis and control.

WU Jiaoyi, born in 1984, is currently a PhD candidate at Theory of Lubrication and Bearing Institute, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing Systems, Xi’an Jiaotong University, China. His research interests include tribology system and intelligent monitoring.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, T., Peng, Y., Sheng, C. et al. Intelligent identification of wear mechanism via on-line ferrograph images. Chin. J. Mech. Eng. 27, 411–417 (2014). https://doi.org/10.3901/CJME.2014.02.411

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3901/CJME.2014.02.411

Keywords

Search

Navigation