American Journal of Mathematical and Computational Sciences  
Manuscript Information
 
 
Noise Feature Extraction and Analysis of Silicone Oil Fan on Diesel Engine Based on Non-negative Tensor Factorization
American Journal of Mathematical and Computational Sciences
Vol.5 , No. 1, Publication Date: Feb. 3, 2020, Page: 1-8
288 Views Since February 2, 2020, 106 Downloads Since Feb. 2, 2020
 
 
Authors
 
[1]    

Xiaocong Meng, Engineering Research Institute, Guangxi Yuchai Machinery Co., Ltd, Yulin, China.

[2]    

Xianglin Lu, Engineering Research Institute, Guangxi Yuchai Machinery Co., Ltd, Yulin, China.

[3]    

Haijun Wang, Engineering Research Institute, Guangxi Yuchai Machinery Co., Ltd, Yulin, China.

 
Abstract
 

The rotate speed of the electronic-control silicone oil fan directly affects the passing-by noise of the GB 1495 requirement of the load truck in China. Especially, when the silicone oil fan is directly connected to the main shaft of diesel engine, the noise level is dramatically increases, which makes the spectrum characteristics become complex and difficult to identify for engineer, resulting in the inaccurate control of the rotate speed of the fan. It is necessary to find a high- efficiency method to analyze the frequency of the fault. In this paper, the method of non-negative tensor factorization (NTF) is used to decompose the data for fault analysis. According to the geometric model of NTF, the fixed-point alternating least squares is selected to calculate the decomposed factors after the data transmitted to a three dimensional tensor of the hyperspectral data. While all the factors are computed the sparse local bispectrum can be reconstructed with khatri-rao product between the factors, which also be named basic local feature. Experiments show that the calculation error decreases with the increase of tensor cardinal dimension, the error is still less than 9%, and the maximum proportion of the value of sparse bispectrum extracted is only 0.71%, which proves that the analysis method of non-negative tensor factorization for bispectrum extraction is a well solution.


Keywords
 

Silicone Oil Fan, Non-negative Tensor Factorization, Sparse, Bispectrum Feature


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