A Feature Extraction Method of Rolling Bearings Faults based on PCA and Power Spectrum Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 681-688 ◽  
Author(s):  
Su Yang ◽  
Yuan Zhonghu ◽  
Qi Xiaoxuan
Keyword(s):  
Feature Extraction ◽  
Power Spectrum ◽  
Spectrum Analysis ◽  
Extraction Method ◽  
Power Spectrum Analysis ◽  
Rolling Bearings ◽  
Feature Extraction Method

scholarly journals A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

2016 ◽  
Vol 16 (3) ◽  
pp. 149-159 ◽  
Author(s):  
Haifeng Huang ◽  
Huajiang Ouyang ◽  
Hongli Gao ◽  
Liang Guo ◽  
Dan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Signal To Noise Ratio ◽  
Experimental Studies ◽  
Vibration Signal ◽  
Noise Sources ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Spectral Kurtosis

Abstract Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

The Feature Extraction Method in Power Spectrum Images for Connector Parts

2015 ◽  
Vol 3 (4) ◽  
pp. 154-160
Author(s):  
Shuichiro Kito ◽  
Koosuke Hattori ◽  
Ryo Taguchi ◽  
Taizo Umezaki
Keyword(s):  
Feature Extraction ◽  
Power Spectrum ◽  
Extraction Method ◽  
Feature Extraction Method

scholarly journals A Novel Fault Feature Extraction Method for Bearing Rolling Elements Using Optimized Signal Processing Method

2021 ◽  
Vol 11 (19) ◽  
pp. 9095
Author(s):  
Weihan Li ◽  
Yang Li ◽  
Ling Yu ◽  
Jian Ma ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Power Spectrum ◽  
Extraction Method ◽  
Grid Search ◽  
Feature Extraction Method ◽  
Adaptive Parameter ◽  
Rolling Element ◽  
Rolling Elements ◽  
Weak Fault ◽  
Fault Feature Extraction

A rolling element signal has a long transmission path in the acquisition process. The fault feature of the rolling element signal is more difficult to be extracted. Therefore, a novel weak fault feature extraction method using optimized variational mode decomposition with kurtosis mean (KMVMD) and maximum correlated kurtosis deconvolution based on power spectrum entropy and grid search (PGMCKD), namely KMVMD-PGMCKD, is proposed. In the proposed KMVMD-PGMCKD method, a VMD with kurtosis mean (KMVMD) is proposed. Then an adaptive parameter selection method based on power spectrum entropy and grid search for MCKD, namely PGMCKD, is proposed to determine the deconvolution period T and filter order L. The complementary advantages of the KMVMD and PGMCKD are integrated to construct a novel weak fault feature extraction model (KMVMD-PGMCKD). Finally, the power spectrum is employed to deal with the obtained signal by KMVMD-PGMCKD to effectively implement feature extraction. Bearing rolling element signals of Case Western Reserve University and actual rolling element data are selected to prove the validity of the KMVMD-PGMCKD. The experiment results show that the KMVMD-PGMCKD can effectively extract the fault features of bearing rolling elements and accurately diagnose weak faults under variable working conditions.

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