A Rotor Fault Feature Extraction Method Based on the Hilbert Marginal Spectrum

2012 ◽  
Vol 201-202 ◽  
pp. 255-258 ◽  
Author(s):  
Jian Wu Wang ◽  
Feng Zou
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Frequency Component ◽  
Rotor System ◽  
Frequency Resolution ◽  
Analysis Method ◽  
Feature Extraction Method ◽  
Marginal Spectrum ◽  
Fault Feature Extraction ◽  
Feature Frequency

In the paper, a fault feature extraction method for rotor system is proposed based on Hilbert marginal spectrum. Compared with the spectrum analysis method via Fourier transformation, it is more effective for the rotating machinery vibrating signal analysis. Extracting the rotor system fault feature frequency from Hilbert marginal spectrum can not only enhance the frequency resolution, but also remove other unrelated frequency component, so as to make the spectrum peak of the fault feature frequency more obviously, and the analysis diagnosis results more accurately. This method result is applied to the fault feature extraction and diagnosis of the rotor system, and the analysis results of the experiment signal verify the validity of this method.

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.

A Fault Feature Extraction Method Based on LMD and MSE for Reciprocating Compressor

2014 ◽  
Vol 530-531 ◽  
pp. 345-348
Author(s):  
Min Qiang Xu ◽  
Hai Yang Zhao ◽  
Jin Dong Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Vibration Signal ◽  
Multiscale Entropy ◽  
Reciprocating Compressor ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Euclidean Distances ◽  
Coupling Characteristics ◽  
Fault Feature Extraction

This paper presents a feature extraction method based on LMD and MSE for reciprocating compressor according to the strong nonstationarity, nonlinearity and features coupling characteristics of vibration signal. The vibration signal was decomposed into a set of PFs, and then multiscale entropy of the first several PFs were calculated as feature vectors with different scale factors. Based on the maximum of average Euclidean distances, the feature vectors which have the best divisibility were selected. The feature vectors of reciprocating compressor at different bearing clearance states were extracted using this method, and superiority of this method is verified by comparing with the results of sample entropy.

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