Oil Debris Signal Detection Based on Integral Transform and Empirical Mode Decomposition

Authors: Chuan Li, Ming Liang

Abstract:

Oil debris signal generated from the inductive oil debris monitor (ODM) is useful information for machine condition monitoring but is often spoiled by background noise. To improve the reliability in machine condition monitoring, the high-fidelity signal has to be recovered from the noisy raw data. Considering that the noise components with large amplitude often have higher frequency than that of the oil debris signal, the integral transform is proposed to enhance the detectability of the oil debris signal. To cancel out the baseline wander resulting from the integral transform, the empirical mode decomposition (EMD) method is employed to identify the trend components. An optimal reconstruction strategy including both de-trending and de-noising is presented to detect the oil debris signal with less distortion. The proposed approach is applied to detect the oil debris signal in the raw data collected from an experimental setup. The result demonstrates that this approach is able to detect the weak oil debris signal with acceptable distortion from noisy raw data.

Keywords: Integral transform, empirical mode decomposition, oil debris, signal processing, detection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081587

References:

[1] S. Greenfield, "Oil debris monitoring for the Eurofighter 2000 aircraft," Proceedings of Condition Monitoring 2001 Conference, Oxford, England, pp. 254-266, Jun. 2001.
[2] J. L. Miller and D. Kitaljevich, "In-line oil debris monitor for aircraft engine condition assessment," 2000 IEEE Aerospace Conference Proceedings, vol. 6, pp. 49-56, Mar. 2000.
[3] X. Fan, M. Liang and T. Yeap, "A joint time-invariant wavelet transform and kurtosis approach to the improvement of in-line oil debris sensor capability," Smart Materials & Structures, vol. 18, no. 8, pp. 085010, Aug. 2009.
[4] H. B. Hong and M. Liang, "A fractional calculus technique for on-line detection of oil debris," Measurement Science & Technology, vol. 19, no. 5, pp. 055703, May 2008.
[5] I. S. Bozchalooi and M. Liang, "In-line identification of oil debris signals: an adaptive subband filtering approach," Measurement Science & Technology, vol. 21, no. 1, pp. 015104, Jan. 2010.
[6] I. S. Bozchalooi and M. Liang, "Oil debris signal analysis based on empirical mode decomposition for machinery condition monitoring," Proceedings of American Control Conference 2009, vol. 1-9, pp. 4310-4315, Jun. 2009.
[7] R. W. Kempster and D. B. George, "Method and apparatus for detecting particles in a fluid having coils isolated from external vibrations," U.S. patent No. 5,444,367, 1995
[8] N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, "The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis," Proc. Roy. Soc. Lond. A, vol. 454, no. 1971, pp. 903-995, Mar. 1998.
[9] Z. H. Wu, N. E. Huang, S. R. Long and C. K. Peng, "On the trend, detrending, and variability of nonlinear and nonstationary time series," Proceedings of the National Academy of Sciences of the United States of America. vol. 104, no. 38, pp. 14889-14894, Sep. 2007.
[10] C. S. Qu, T. Z. Lu and Y. Tan, "A modified empirical mode decomposition method with applications to signal de-noising," Acta Automatica Sinica, vol. 36, no. 1, pp. 67-73, Jan. 2010.
[11] B. N. Krupa, M. A. M. Ali and E. Zahedi, "The application of empirical mode decomposition for the enhancement of cardiotocograph signals," Physiological Measurement, vol. 20, no. 8, pp. 729-743, Aug. 2009.
[12] S. G. Mallat, "A wavelet tour of signal processing," San Diego: Academic, 1998.