@article{(Open Science Index):https://publications.waset.org/pdf/13688,
	  title     = {Oil Debris Signal Detection Based on Integral Transform and Empirical Mode Decomposition},
	  author    = {Chuan Li and  Ming Liang},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {5},
	  number    = {4},
	  year      = {2011},
	  pages     = {749 - 753},
	  ee        = {https://publications.waset.org/pdf/13688},
	  url   	= {https://publications.waset.org/vol/52},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 52, 2011},