Oil Debris Signal Detection Based on Integral Transform and Empirical Mode Decomposition
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.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62698", author = "Chuan Li and Ming Liang", title = "Oil Debris Signal Detection Based on Integral Transform and Empirical Mode Decomposition", 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.", volume = "5", number = "4", pages = "846-5", }