Weak Fault Feature Extraction of Rolling Bearings Using Local Mean Decomposition-Based Multilayer Hybrid Denoising

2017 ◽  
Vol 66 (12) ◽  
pp. 3148-3159 ◽  
Author(s):  
Jianbo Yu ◽  
Jingxiang Lv
Keyword(s):  
Feature Extraction ◽  
Rolling Bearings ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Weak Fault ◽  
Fault Feature Extraction

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 The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Yugang Fan ◽  
Xiaodong Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Dominant Frequency ◽  
Working Process ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Common Time ◽  
Local Mean ◽  
Fault Feature Extraction

Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

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.

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