scholarly journals Impulsive Gear Fault Diagnosis Using Adaptive Morlet Wavelet Filter Based on Alpha-Stable Distribution and Kurtogram

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 72283-72296 ◽  
Author(s):  
Mang Gao ◽  
Gang Yu ◽  
Tao Wang
Keyword(s):  
Fault Diagnosis ◽  
Stable Distribution ◽  
Morlet Wavelet ◽  
Wavelet Filter ◽  
Alpha Stable Distribution ◽  
Gear Fault ◽  
Gear Fault Diagnosis

scholarly journals Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Lei Xiao ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Stable Distribution ◽  
Planetary Gearbox ◽  
Alpha Stable Distribution ◽  
Gear Fault

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

Domain Adaptive Sparse Transformer for Aeroengine Bevel Gear Fault Diagnosis

2021 ◽  
Author(s):  
Yasong Li ◽  
Zheng Zhou ◽  
Chuang Sun ◽  
Ruqiang Yan ◽  
Xuefeng Chen
Keyword(s):  
Fault Diagnosis ◽  
Bevel Gear ◽  
Gear Fault ◽  
Gear Fault Diagnosis
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