Time–frequency interpretation of multi-frequency signal from rotating machinery using an improved Hilbert–Huang transform. (March 2016)

• Record Type: Journal Article
• Authors: Zhang, Yan; Tang, Baoping; Xiao, Xin
• Is Part Of: Measurement. Volume 82(2016:Mar.)
• Page Start: 221
• Page End: 239
• Publication Date: 2016-03
• Subjects: Time–frequency analysis -- Hilbert–Huang transform -- Empirical mode decomposition -- Rotating machinery; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.01.001
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A procedure for separating the energy-dominated components is proposed. A correlation coefficient-based criterion is proposed to select the relevant IMFs. The method has been evaluated using simulated, lab experimental, and on-site measured datasets. The proposed method does improve the traditional HHT and displays the signal features clearly. Abstract: The Hilbert–Huang transform (HHT) has proven to be a promising tool for the analysis of non-stationary signals commonly occurred in industrial machines. However, in practice, multi-frequency intrinsic mode functions (IMFs) and pseudo IMFs are likely generated and lead to grossly erroneous or even completely meaningless instantaneous frequencies, which raise difficulties in interpreting signal features by the HHT spectrum. To enhance the time–frequency resolution of the traditional HHT, an improved HHT is proposed in this study. By constructing a bank of partially overlapping bandpass filters, a series of filtered signals are obtained at first. Then a subset of filtered signals, each associated with certain energy-dominated components, are selected based on the maximal-spectral kurtosis–minimal-redundancy criterion and the information-related coefficient, and further decomposed by empirical mode decomposition to extract sets of IMFs. Furthermore, IMF selection scheme is applied to select the relevant IMFs on which the HHT spectrum is constructed. The novelty of this method is that the HHT spectrum is just constructed with the relevant, almost monochromatic IMFs rather than with the IMFs possibly with multiple frequency components or with pseudo components. The results on the simulated data, test rig data, and industrial gearbox data show that the proposed method is superior to the traditional HHT in feature extraction and can produce a more accurate time–frequency distribution for the inspected signal.