scholarly journals A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 141633-141647 ◽  
Author(s):  
Zhixing Li ◽  
Xiandong Liu ◽  
Tian He ◽  
Yingchun Shan
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Periodic Potential ◽  
Resonance Method ◽  
Gear Fault ◽  
Gear Fault Diagnosis

scholarly journals A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

2019 ◽  
Author(s):  
Zhixing Li ◽  
Xiandong Liu ◽  
Tian He ◽  
Yingchun Shan
Keyword(s):  
Stochastic Resonance ◽  
Background Noise ◽  
Ant Colony Algorithm ◽  
Periodic Potential ◽  
Potential Model ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Noise Energy ◽  
Gear Fault ◽  
Potential Structure

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

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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