scholarly journals Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1482 ◽  
Author(s):  
Xianglong Chen ◽  
Fuzhou Feng ◽  
Bingzhi Zhang
Keyword(s):  
Feature Extraction ◽  
Rolling Bearings ◽  
Weak Fault ◽  
Fault Feature Extraction

scholarly journals Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huaitao Shi ◽  
Yangyang Li ◽  
Peng Zhou ◽  
Shenghao Tong ◽  
Liang Guo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Barrier Height ◽  
Stochastic Resonance ◽  
Working Conditions ◽  
Potential Well ◽  
Rolling Bearings ◽  
Input Signals ◽  
Weak Fault ◽  
Fault Feature Extraction

The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

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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