A Fault Feature Extraction Method for Machine Health Diagnosis Using Manifold Learning

2012 ◽  
Author(s):  
Liu Yongbin ◽  
Zhu Lin ◽  
He Qingbo ◽  
Zhang Ping ◽  
Zhao Jiwen
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Machine Health ◽  
Fault Feature Extraction ◽  
Health Diagnosis

Feature Extraction Method for Rolling Bearing’s Week Fault Based on Kalman Filter and FSK

2014 ◽  
Vol 574 ◽  
pp. 684-689
Author(s):  
Zhi Chuan Liu ◽  
Li Wei Tang ◽  
Li Jun Cao
Keyword(s):  
Feature Extraction ◽  
Kalman Filter ◽  
Extraction Method ◽  
Model Parameters ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Feature Extraction Method ◽  
Spectral Kurtosis ◽  
Band Pass ◽  
Fault Feature Extraction

Aiming at the problem that traditional demodulated resonance technology has the deficiency of difficulty to choose the parameters of band-pass filter, Kalman filter technology and fast spectral kurtosis were combined for fault feature extraction of rolling bearing. AR model was firstly built with gearbox original vibration signals, and then model order was ascertained with AIC formula, and finally model parameters were calculated with least-squares method. The original signals were pretreated by Kalman filter. Fast spectral kurtosis (FSK) was used to choose parameters of the best band-pass filter, and finally fault diagnosis was achieved by the energy operator demodulation spectrum analysis of band-pass filtered signal. The analysis result of engineering signals indicated that fault feature extraction method based on Kalman filter and fast spectral kurtosis can primely provide a new feature extraction method for rolling bearing’s week fault.

Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning

2014 ◽  
Vol 533 ◽  
pp. 247-251
Author(s):  
Hai Bing Xiao ◽  
Xiao Peng Xie
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Extraction Method ◽  
Classification Performance ◽  
Locally Linear Embedding ◽  
Simulation Research ◽  
Feature Extraction Method ◽  
Linear Embedding ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

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