Machine Condition Monitoring by a Novel Fractal Analysis

Abstract:

The nonlinearities, induced by structural looseness or wear/fatigue of components, are good indicators of the health condition of a machine or structure. However, most existing condition monitoring techniques were initially designed for dealing with linear systems, thus unable to account for these scenarios properly. A few available nonlinear techniques are tried in condition monitoring. However, they are more or less limited owing to either intensive computation or unsatisfactory sensitivity to incipient abnormalities. In view of this, a new fractal analysis-based condition monitoring technique is researched in this paper. Firstly, a few number of fractal analysis methods with efficient computing algorithms are investigated in order to find an ideal one for condition monitoring application. Subsequently, a detailed investigation was conducted to verify the favored method and understand its instantaneous properties, robust performance against noise, and sensitivity to the abnormalities. Finally, following discussing the window width used in practical calculation, the condition monitoring technique developed based on the favored fractal analysis method is validated experimentally. Experiments show that the proposed technique does provide an efficient and successful nonlinear tool for machine operation condition and structural health condition assessment.

• Periodical: Advanced Materials Research (Volumes 452-453)
• Pages: 1434-1440
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.452-453.1434
• Online since: January 2012
• Authors: Wen Xian Yang, Jie Sheng Jiang
• Keywords: Condition Monitoring, Fractal Dimension, Nonlinearity

References

[1] S. Soheil, N.N. Mohammad, D.B. Gregory, F. Tadatoshi and S. Yoshiyuki: Int. J. Nonlinear Mech. Vol. 39 (2004), p.1687.

[2] L. Moniz, J.M. Nichols, C.J. Nichols, M. Seaver, S.T. Trickey, M.D. Todd, L.M. Pecora and L.N. Virgin: J. Sound Vibrat. Vol. 283 (2005), p.295.

DOI: 10.1016/j.jsv.2004.04.016

[3] B.I. Epureanu and S.H. Yin: Computers & Structures Vol. 82 (2004), p.2743.

[4] C. Zhang and M. Imregun: J. Sound Vibrat. Vol. 242 (2001) p.813.

[5] S.T. Trickey, M. Todd, M. Seaver and J. Nichols: Proc. SPIE 9th Smart Struct. Materials Conf. Vol. 1 (2002), p.1.

[6] M.E. Kirlangic, D. Perez, S. Kudryavtseva, G. Griessbach, G. Henning and G. Ivanova: Proc. 23th Conf. IEEE Eng. Med. Biol. Soci. Vol. 2 (2001), p.1573.

[7] V.K. Yeragani, E. Sobolewski, V.C. Jampala, J. Kay, S. Yeragani and G. Igel: Clinic Sci. Vol. 95 (1988), p.295.

DOI: 10.1042/cs19970268

[8] W.J. Wang, Z.T. Wu and J. Chen: Nonlinear Dynamics Vol. 25 (2001), p.383.

[9] H. Froehling, J.P. Crutchfield, J.D. Farmer, N.H. Packard and R. Shaw: Physica D 3 (1981), p.605.

[10] M. Katz: Comput. Biol. Med. Vol. 18 (1988), p.145.

[11] L.J. Hadjileontiadis, E. Douka and A. Trochidis: Mech. Syst. Signal Proc. Vol. 19 (2005), p.659.

[12] C. Sevcik: Complexity International [online] Vol. 5 (1998), Information on http: /journal-ci. csse. monash. edu. au/ci/vol05/sevcik/sevcik. html.

[13] J. Gnitecki and Z. Moussavi: Proc. 25th Conf. IEEE Eng. Med. Biol. Soci. (2003), p.2420.