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20-04-2017 | Original Article | Issue 3/2017

Chinese Journal of Mechanical Engineering 3/2017

Optimization of the End Effect of Hilbert-Huang transform (HHT)

Journal:
Chinese Journal of Mechanical Engineering > Issue 3/2017
Authors:
Chenhuan LV, Jun ZHAO, Chao WU, Tiantai GUO, Hongjiang CHEN
Important notes
Supported by National Natural Science Foundation of China (Grant No. 51375467), and Quality Inspection of Public Welfare Industry Research Projects, China (Grant No. 201410009)

Abstract

In fault diagnosis of rotating machinery, Hilbert-Huang transform (HHT) is often used to extract the fault characteristic signal and analyze decomposition results in time-frequency domain. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false IMF (Intrinsic Mode Function). To counter such problems in HHT, a new method is put forward to process signal by combining the generalized regression neural network (GRNN) with the boundary local characteristic-scale continuation (BLCC). Firstly, the improved EMD (Empirical Mode Decomposition) method is used to inhibit the end effect problem that appeared in conventional EMD. Secondly, the generated IMF components are used in HHT. Simulation and measurement experiment for the cases of time domain, frequency domain and related parameters of Hilbert-Huang spectrum show that the method described here can restrain the end effect compared with the results obtained through mirror continuation, as the absolute percentage of the maximum mean of the beginning end point offset and the terminal point offset are reduced from 30.113% and 27.603% to 0.510% and 6.039% respectively, thus reducing the modal aliasing, and eliminating the false IMF components of HHT. The proposed method can effectively inhibit end effect, reduce modal aliasing and false IMF components, and show the real structure of signal components accurately.

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