An improved HHT with GM(2,1) predictive model for fault detection and diagnosis

This paper presents a new improved Hilbert-Huang Transform(HHT) schemes for fault diagnosis, which plays especially important role in the industrial production nowadays. One of the inevitably traditional problems to handle is end effect in HHT, which is caused by Gibbs´ phenomenon and other mathematical or numerical problems. In order to mitigate the end effect of HHT, a boundary extension method based on the well-known Gray Prediction Model(GM) is introduced. In this article, we adopt a new the HHT optimized with GM(2,1) model. As a second-order oscillation function model, GM(2,1) is capable of forecasting non-monotone fluctuation fault signal. Time response sequence of GM(2,1) will be obtained by estimating the expression of original signal, thus extending both ends of the original signal. Then the extensional original will be transformed by HHT method, acquiring switching signals with a longer sample radius than the quondam original. We have conducted experiments of a bearing fault diagnosis for evaluating the performance of the proposed model. According to the comparison with simulation results of GM(1,1)-HHT and HHT, it is demonstrated that GM(2,1)-HHT can significantly reduce end effects and narrows remainder range.