Journal
MEASUREMENT
Volume 139, Issue -, Pages 226-235Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.02.072
Keywords
EEMD; Wavelet denoising; Mean of the standardized accumulated modes; Modulation signal bispectrum; Fault diagnosis
Funding
- National Natural Science Foundation of China [51605133, 51705127]
- Hebei Provincial International Science and Technology Cooperation Program of China [17394303D]
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To extract impulsive feature from vibration signals with strong background noise and interference components for accurate bearing diagnostics. A multi-stage noise reduction method is proposed based on ensemble empirical mode decomposition (EEMD), wavelet thresholding (WT) and modulation signal bispectrum (MSB). Firstly, noisy vibration signals are applied with EEMD to obtain a list of intrinsic mode functions (IMFs) that leverage the desired modulation components to different degrees. Then, a number of initial IMFs in the high frequency range, which are separated by using the mean of the standardized accumulated modes (MSAM) to have more modulation contents, are further denoised by a wavelet thresholding approach. These cleaned IMFs in the high frequency are combined with the low frequency IMFs to construct a pre-denoised signal that maintains the modulation properties of the raw signal. Finally, modulation signal bispectrum (MSB) is used to extract the modulation signature by suppressing further the residual random noise and deterministic interference components. This multi-stage noise reduction method was validated through a simulation study and two experimental fault cases studies of rolling element bearing. The results were more accurate and reliable in diagnosing the bearing inner and outer race defects in comparison with the individual use of the state-of-the-art EEMD and MSB. (C) 2019 Elsevier Ltd. All rights reserved.
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