Sparse component analysis‐based under‐determined blind source separation for bearing fault feature extraction in wind turbine gearbox

2017 ◽  
Vol 11 (3) ◽  
pp. 330-337 ◽  
Author(s):  
Chun‐zhi Hu ◽  
Qiang Yang ◽  
Miao‐ying Huang ◽  
Wen‐jun Yan
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Wind Turbine Gearbox ◽  
Bearing Fault ◽  
Sparse Component Analysis ◽  
Fault Feature Extraction

Research on Fault Feature Extraction Method of the Wind Turbine Gearbox Based on SVD and Improved HHT

2012 ◽  
Vol 220-223 ◽  
pp. 785-788
Author(s):  
Chang Zheng Chen ◽  
Quan Gu ◽  
Bo Zhou
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Wind Turbine ◽  
Extraction Method ◽  
Random Noise ◽  
Singular Value ◽  
Wind Turbine Gearbox ◽  
Feature Extraction Method ◽  
Value Decomposition ◽  
Fault Feature Extraction

This paper researches fault feature extraction method based on singular value decomposition and the improved HHT method for non-stationary characteristics of wind turbine gearbox vibration signal. Firstly, through the signal phase space reconstruction, the singular value decomposition as a pre-filter, to preprocessing the signal, effectively weaken the random noise. Then using EEMD to improve the HHT method, decompose the denoising signal into a series of different time scales component of intrinsic mode functions. The fault characteristics of the signal are extracted by the Hilbert transform. Finally, simulating gearbox fault experiment to verify the effectively of the proposed method.

Application of Independent Component Analysis in Machine Fault Diagnosis

2014 ◽  
Vol 905 ◽  
pp. 524-527
Author(s):  
Feng Miao ◽  
Rong Zhen Zhao
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Learning Rule ◽  
Component Analysis ◽  
Independent Component ◽  
Machine Fault Diagnosis ◽  
Machine Fault

A novel fast algorithm for lndependent Component Analysis is introduced, which can be used for blind source separation and machine fault diagnosis feature extraction. It is shown how a neural network learning rule can be transformed into a fixed-point iteration, which provides an algorithm that is very simple, does not depend on any user-defined parameters, and is fast to converge to the most accurate solution allowed by the data. The purpose of this paper is to review the application of blind source separation in the machine fault diagnosis,including the following aspects: noise elimination and extraction of the weak signals,the separation of multi-fault sources,redundancy reduction,feature extraction and pattern classification based on independent component analysis. And its application in machine fault diagnosis is illustrated by the examples. In addition, some prospects about using blind source separation for machine fault diagnosis are discussed.

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