As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.
In this paper, we studied voice signal with Gaussian noise reduction. Based on signal analysis and reconstruction principle. Application of Hilbert-Huang transform(HHT), analyzed voice signal with Gaussian noise. And comprised with wavelet transform(WT), obtained the correlation coefficient between HHT noise reduction signal and excluding the noise signal was 0.8986, WT was 0.7889. The results showed that in the voice signal with Gaussian noise, compared HHT and wavelet analysis correlation analysis values, HHT noise reduction ability was 10% higher than WT. This paper provided a new analytic method to the voice signal noise reduction and enhanced the accuracy of it.
In this paper, the abnormal sound of bearing pad engine signal was studied. As the car engine signal is nonlinear and non-stationary, Hilbert-Huang transform method diagnosis the normal and bearing pad abnormal sound engine signal was used. Hilbert spectrum and time-frequency distribution 3-d map was got. Through these we knew normal engine signal frequency was 210 Hz, the abnormal sound of bearing pad engine signal frequency mainly concentrated in the 500 Hz, 1500 Hz and 2700 Hz. The results showed that the cause of abnormal sound was bearing pad wear. Sound signal was used in this experiment, it was easy to get, and the HHT analysis can separate cause abnormal sound of the high frequency component from the abnormal sound signal. From analysis of it, the abnormal sound reasons can find easily. It provides a new simple and effective method to the abnormal sound of bearing pad fault diagnosis.
In this paper, the gears fault signal in the engine was studied. Hilbert-Huang transform was applied for the gears fault signal analysis. From the experiment, the normal engine frequency of 240 Hz was got and the gears fault signal frequency concentrated in 2800 Hz. Through the study of intrinsic mode function and the Hilbert spectrum, improper meshing gears were the cause of this problem. The results showed that this method can effectively extract the fault feature and found out the cause of the problem. A new effective method is provided for the gears fault diagnosis.
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