Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum

2011 ◽  
Vol 25 (5) ◽  
pp. 1773-1785 ◽  
Author(s):  
Yang Ming ◽  
Jin Chen ◽  
Guangming Dong
Keyword(s):  
Feature Extraction ◽  
Wiener Filter ◽  
Rolling Bearing ◽  
Weak Fault ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

scholarly journals Rolling Bearing Feature Extraction Method Based on Improved Intrinsic Time-Scale Decomposition and Mathematical Morphological Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2719
Author(s):  
Jianpeng Ma ◽  
Guodong Chen ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guang-Zhu Zhang
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Intrinsic Time ◽  
Extraction Scheme ◽  
Time Scale Decomposition ◽  
Adaptive Feature Extraction ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

scholarly journals A Hybrid Approach for Weak Fault Feature Extraction of Gearbox

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 16616-16625 ◽  
Author(s):  
Yu Wei ◽  
Minqiang Xu ◽  
Xianzhi Wang ◽  
Wenhu Huang ◽  
Yongbo Li
Keyword(s):  
Feature Extraction ◽  
Hybrid Approach ◽  
Weak Fault ◽  
Fault Feature Extraction

scholarly journals Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Long Zhang ◽  
Binghuan Cai ◽  
Guoliang Xiong ◽  
Jianmin Zhou ◽  
Wenbin Tu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Diagnostic Methods ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Transmission Path ◽  
The Individual ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

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