A new method of gear fault diagnosis in strong noise based on multi-sensor information fusion

2014 ◽  
Vol 22 (6) ◽  
pp. 1504-1515 ◽  
Author(s):  
Gang Cheng ◽  
Xi-hui Chen ◽  
Xian-lei Shan ◽  
Hou-guang Liu ◽  
Chang-fei Zhou
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
New Method ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Sensor Information

Gear Fault Signal Modeling Study Based on Probability Box Theory

2014 ◽  
Vol 599-601 ◽  
pp. 1225-1228
Author(s):  
An Liu ◽  
Yi Du ◽  
Jia Man Ding
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Failure Modes ◽  
New Method ◽  
Signal Modeling ◽  
Gear Fault ◽  
Rich Information ◽  
The Rich ◽  
Almost All

Gears typical failure modes and fault diagnosis methods were summarized, and their characteristics and deficiency were contrasted. As almost all method need feature extraction before information fusion, the rich information in original signals were lost in this process. Another difficult problems of information fusion is the the space-time registration. The probability box theory can be a new method to solve the above two problems. The gears fault signal modeling method based on probability box theory were then proposed. Finally the prospects and study directions of this method’s applications in gear box fault diagnosis were proposed.

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information

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
Sign in / Sign up

Export Citation Format

Share Document