A methodology for online wear debris morphology and composition analysis

Abstract

Online or inline detection of basic debris features, i.e. size, quantity, size distribution, shape, and compositions simultaneously with real time diagnostics is one of the possible ways to perform wear debris analysis with high reliability. At present many techniques and sensors are available that can perform near real time detection and diagnostics for debris quantitative features. But to perform real time detection and diagnostics for features like shape and composition still requires a reliable technical concept.

In this article a new technique for online wear debris shape and composition analysis is described. The developed technique is a combination of hardware and software based on imaging technology for shape and composition detection. Rule-based algorithms are used to perform near real time debris analysis diagnostics. An experimental study is also presented that shows the possible potential of the developed technique on real applications.

Keywords

wear debris debris basic features image processing and real time diagnostics

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References

Rao

Handbook of condition monitoring, 1996 (Elsevier advanced technology, Oxford).

Dempsey

Afjeh

Integrating oil debris and vibration gear damage detection technologies using fuzzy logic. J. Am. Helicopter Soc., 2004, 49, 109–116.

Khan

M. A.

Starr

A. G.

Cooper

Academic and commercial efforts in wear debris analysis automation (WDAA). INSIGHT, 2007, 49, 726–732.

Raadnui

Wear particle analysis — utilization of quantitative computer image analysis: A review. Tribol. Int., 2005, 38, 871–878.

Khan

M. A.

Starr

A. G.

Cooper

Wear debris: Basic features and machine health diagnostics. INSIGHT, 2006, 48, 470–476.

Muir

Taylor

Oil debris monitoring for aero-derivative gas turbines. ASME Power Div. PWR, 1997, 32, 547–553.

Harvey

T. J.

Wood

R. J. K.

Powrie

H. E. G.

Electrostatic wear monitoring of rolling element bearings. Wear, 2007, 263, 1492–1501.

8 Anon. Condition monitoring spotlight. INSIGHT, 1997, 39, 533.

Reintjes

Mahon

Duncan

M. D.

Tankersley

L. L.

Schultz

Howard

P. L.

Stevens

C. L.

Lasernet optical oil debris monitor. In Proceedings of the JOAP International Condition Monitoring Conference, Alabama, USA, 20-24 April 1994, pp. 110–116.

10.

Lukas

Anderson

D. P.

Sebok

Filicky

Laser net fines — a new tool for the oil analysis toolbox instrument review. Practicing Oil Anal. Mag., September 2002, issue 200209.

11.

11 MathWorks.com Matlab® image processing toolbox guide. July 2007, available from http://www.mathworks.com/access/helpdesk/help/toolbox/images/index.html/access/helpdesk/help/toolbox/images/stdfilt.html.

12.

Roylance

B. J.

Hunt

T. M.

Wear debris analysis, 1999 (Coxmoor Publishing Company, Oxford).

13.

Hunt

T. M.

Handbook of wear debris analysis and particle detection in liquids, 1993 (Elsevier Applied Science, London).

14.

Gebarin

Fitch

Origin of spherical particles in lubricants. Practicing Oil Anal. Mag., March 2005, issue 200503.

15.

Ding

Determining fatigue wear using wear particle analysis tools. Practicing Oil Anal. Mag., September 2003, issue 200309.

16.

Yuansheng

Chengbiao

Spherical particles generated during the running-in period of diesel engine. Wear, 1989, 131, 315–328.

17.

Davies

Handbook of condition monitoring techniques and methodology, 1998 (Chapman & Hall, London).

18.

Myshkin

N. K.

Kong

Grigoriev

A. Y.

Yoon

E. S.

The use of color in wear debris analysis. Wear, 2001, 251, 1218–1226.

19.

Yonghui

Research on wear debris color recognition with factor analysis. Lubr. Eng., 2006, 6, 112–114.

20.

Myshkin

N. K.

Grigoriev

A. Y.

Markova

L. V.

Condition monitoring of tribosystems by wear debris analysis. Int. J. Appl. Mech. Eng., 2002, 7, 923–947.

21.

X.-Y.

Z.-Y.

D.-G.

Chen

Wang

Yan

X.-P.

Debris analysis with PCA in an HIS color model. J. Wuhan Univ. Technol, 2005, 29, 1–4.

22.

Laghari

M. S.

Surface feature recognition of wear debris. In the 15th Australian Joint Conference on Artificial intelligence Canberra, Australia, 2-6 December 2002, pp. 627–637 (Springer-Verlag, Germany).

23.

Khan

M. A.

Starr

A. G.

Theoretical and experimental working life comparison for a helical gear under linear pitting failure. Appl. Mech. Mater., 2007, 7-8, 95–100.

24.

Dempsey

Gear damage detection using oil debris analysis. Gear Technol., 2003, 20, 12–17.

25.

25 Neale

, et al. Guide to the condition monitoring of machinery, 1979, available from http://www.tribology.co.uk/publish/b011.htm (Crown Publishers, London).