Time–frequency interpretation of multi-frequency signal from rotating machinery using an improved Hilbert–Huang transform |
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| Affiliation: | 1. School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China;2. School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China;3. Tianjin Key Laboratory for Photoelectric Materials and Devices, Tianjin University of Technology, Tianjin 300384, China;1. School of Energy, Power and Mechanical Engineering, North China Electric Power University, Changping District, Beijing 102206, China;2. Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China;1. Dept. of Information Technology and Electrical Engineering, University of Naples “Federico II”, Via Claudio 6, 80125 Napoli, Italy;2. Dept. of Electrical and Information Engineering, University of Cassino and Southern Lazio, Via G. Di Biasio 43, 03043 Cassino, FR, Italy;1. College of Information and Electrical Engineering, Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, 100083 Beijing, China;2. Department of Agricultural Engineering, The University of Bonn, 53115 Bonn, Germany;1. State Key Lab of Mechanical Transmission, Chongqing University of China, Chongqing 400030, People’s Republic of China;2. Southwest China Research Institute of Electronic Equipment, Chengdu, Sichuan Province 610036, People’s Republic of China;3. Industry Manufacture College, Chengdu University of China, Chengdu, Sichuan Province 610106, People’s Republic of China |
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| 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. |
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| Keywords: | Time–frequency analysis Hilbert–Huang transform Empirical mode decomposition Rotating machinery |
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