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| A New Method to Optimize Endpoint Effect in HHT |
| ZHAO Jun,LI Lin-feng,GUO Tian-tai,WANG Dao-dang,KONG Ming |
| College of Metrology & Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China |
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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 analysis. However, end effect occurs in HHT, which leads to a series of problems such as modal aliasing and false intrinsic mode function(IMF). To solve this problem, a novel method to optimize end effect in HHT through combination of generalized regression neural network (GRNN) and boundary local characteristic-scale continuation (BLCC) to extend signal is proposed, then followed by empirical mode decomposition (EMD). Simulation and measurement experiment for the conditions of time domain, frequency domain and related parameters of Hilbert-Huang spectrum verified the effectiveness of the proposed method through comparison with the results obtained by mirror continuation. The experimental results show that the method can effectively inhibit end effect, reduce modal aliasing and false IMF components, and accurately show the real structure of signal components.
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Received: 09 December 2014
Published: 29 July 2016
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2016, Vol. 37 
