FILM THICKNESS MEASUREMENT OF MECHANICAL SEAL BASED ON CASCADED ARTIFICIAL NEURAL NETWORK RECOGNITION MODEL

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 7 , ISSUE 4 (December 2014) > List of articles

FILM THICKNESS MEASUREMENT OF MECHANICAL SEAL BASED ON CASCADED ARTIFICIAL NEURAL NETWORK RECOGNITION MODEL

Erqing Zhang * / Pan Fu / Kesi LI / Xiaohui Li / Zhongrong Zhou

Keywords : Mechanical Seal, Film Thickness, Eddy Current, Acoustic Emission, Empirical Mode Decomposition, Artificial Neural Network.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 4, Pages 1,870-1,889, DOI: https://doi.org/10.21307/ijssis-2017-737

License : (CC BY-NC-ND 4.0)

Received Date : 22-August-2014 / Accepted: 02-November-2014 / Published Online: 01-December-2014

ARTICLE

ABSTRACT

Mechanical seal end faces are separated by a thin fluid film. The thickness of this film must
be optimized so as to preventing the serious friction of two end faces and minimizing the fluid leakage.
The micro scope condition monitoring of end face is of importance to ensure mechanical seals
run normally. A method for measuring the film thickness of end faces and detecting the friction of end
faces of mechanical seal has been presented in this paper. Eddy current sensors embedded in the
stationary ring of mechanical seals are used to directly measure the thickness of the liquid-lubricated
film. The Eddy current signal is decomposed by empirical mode decomposition into a series of intrinsic
mode function. The information reflecting the film thickness is obtained by eliminating the false
intrinsic mode function components. Acoustic emission sensor placed on the lateral of stationary ring is used to detect the friction of end faces. In order to decrease the acoustic emission signal’s noise,
wavelet packet and kernel principal component analysis are used to extract the data features. Then
cascaded decision is presented to improve the recognition rate of artificial neural network, by which the
film thickness can be estimated accurately. With a set of tests, the results demonstrate that the method
is effective. It can be widely used to take measurement of the film thickness in industrial field.

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REFERENCES

[1].Sun Jianjun, Wei Long, Gu Boqing. The Development Course and The Research Trend of
Mechanical Seal. Lubrication Engineering, Lubrication Engineering, Vol.4, 2004, pp.128-134.
[2] G. Astridge and M. D. Longfield. Capacitance measurement and oil film thickness in a largeradius
disc and ring machine, Vol.182, No.14, 1967, pp.89-96.
[3] P. C. Chu and A. Cameron. Flow of electric current through lubricated contacts. ASLE
Transactions, Vol.10, No.3, 1967, pp.226-234.
[4] Etsion,I. Experimental of the dynamic behavior of non-contacting coned-face mechanical
seals. ASLE Transactions, Vol.27, No.3, 1984, pp.263-270.
[5] W. B. Anderson, J. Jarzynski and R. F. Salant. A condition monitor for liquid lubricated
mechanical seals. Tribology Transacitions, Vol.44, No.3, 2001, pp.479-483.
[6] T. Reddyhoff, R.S. Dwyer-Joyce, and P. A new approach for the measurement of film
thickness in liquid face seals. Tribology Transactions, Vol.51, No.2, 2008, pp.140-149.
[7] J. Miettinen and V. Siekkinen. Acoustic emission in monitoring sliding contact behaviour.
Wear, Vol.183, No.2, 1995, pp.897-900.
[8].Cai Yanping, Li Aihua, Wang Tao,et al. Based on The EMD - Wigner Ville - internal
Combustion Engine Vibration Time-frequency Analysis. Journal of Vibration Engineering,
Vol.10, No.4, 2010, pp.430-437.
[9].Xu Dong. Ball Bearing Fatigue Residual Life Analysis and The Forecast Method Research.
Wuhan, Doctor of Engineering thesis, National University of Defense Technology, China, 2011,
pp.205.
[10] Shaojiang Dong and Tianhong Lou. Bearing degradation process prediction based on the
PCA and optimized LS-SVM model. Measurement, Vol.46, No.9, 2013. pp.3143-3152.
[11] K.I. Kim, S. H. Park, and H. J. Kim. Kernel principal component analysis for texture
classification. IEEE Signal Processing Letters, Vol.8, No.2, 2011, pp.39-41.
[12] Weilin Li, Pan Fu, Weiqing Cao. Study on Feature Selection and Identification Method of
Toll Wear States Based on SVM. International Journal on Smart Sensing and Intelligent Systems,
Vol.6, No.2, 2013, pp.448-465.
[13] S.C.Mukhopadhyay, Novel Planar Electromagnetic Sensors: Modeling and Performance
Evaluation, Sensors 2005, 5, 546-579, ISSN 1424-8220 © 2005 by MDPI
http://www.mdpi.org/sensors, 2005.
[14] G. S. Vijay, H.S. Kumar and N.S. Sriram et al. Evaluation of effectiveness of wavelet based
denoising schemes using ANN and SVM for bearing condition classification. Computational
Intelligence and Neuroscience, Vol.12, 2012, pp.46-51.
[15] S.C.Mukhopadhyay, “A Novel Planar Mesh Type Micro-electromagnetic Sensor: Part I -
Model Formulation”, IEEE Sensors journal, Vol. 4, No. 3, pp. 301-307, June 2004.
[16] P. Sam Paul and A. S. Varadarajan. ANN assisted sensor fusion model to predict tool wear
during hard turning with minimal fluid application. IJMMM, Vol.13. No.14, 2013, pp.398-413.
[17] S.C.Mukhopadhyay, “A Novel Planar Mesh Type Micro-electromagnetic Sensor: Part II –
Estimation of System Properties”, IEEE Sensors journal, Vol. 4, No. 3, pp. 308-312, June 2004.
[18] Benny Karunakar and D. L. Datta. Prediction of defects in castings using back propagation
neural networks. IJMIC, Vol.3, No.2, 2008, pp.140-147.
[19] S.C.Mukhopadhyay, “Quality inspection of electroplated materials using planar type micromagnetic
sensors with post processing from neural network model”, IEE Proceedings – Science,
Measurement and Technology, Vol. 149, No. 4, pp. 165-171, July 2002.
[20] Zhao Peng. Study on the vibration fault diagnosis method of centrifugal and system
implementation. Doctor of Engineering thesis, North China Electric Power University(Beijing),
China, 2011, P68.

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