Application of grey relation degree in rotor's fault identification

2012 ◽  
Author(s):  
Wenbin Zhang ◽  
Yanping Su ◽  
Jie Min ◽  
Ruijing Teng ◽  
Yanjie Zhou
Keyword(s):  
Fault Identification ◽  
Grey Relation

scholarly journals Application of EEMD Sample Entropy and Grey Relation Degree in Gearbox Fault Identification

2014 ◽  
Vol 8 (1) ◽  
pp. 402-408
Author(s):  
Wenbin Zhang ◽  
Libin Yu ◽  
Yanping Su ◽  
Jie Min ◽  
Yasong Pu
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Small Sample ◽  
Fault Identification ◽  
Morphological Filter ◽  
Sampled Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Grey Relation

In this paper, a new gearbox fault identification method was proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by mathematical morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EEMD method. Thirdly, some IMFs containing the most dominant fault information were calculated by the sample entropy for four gearbox conditions. Finally, since the grey relation degree has good classified capacity for small sample pattern identification, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gearbox fault diagnosis. It’s suitable for on-line monitoring and fault diagnosis of gearbox.

Intelligent Rotor Fault Identification Method Based on HWD Energy Distribution and Grey Relation Degree

2013 ◽  
Vol 684 ◽  
pp. 373-376
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Energy Distribution ◽  
Vibration Signal ◽  
Fault Identification ◽  
Energy Distributions ◽  
Frequency Bands ◽  
Identification Approach ◽  
Frequency Band Energy ◽  
Fault Information ◽  
Grey Relation ◽  
Feature Frequency

In this paper, a novel comprehensive fault identification approach was proposed based on the harmonic window decomposition (HWD) frequency band energy extraction and grey relation degree. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, due to the energy of vibration signal will change in different frequency bands when fault occurs, therefore, the six feature frequency bands which contain the typical fault information were extracted by harmonic window decomposition that need not decomposition; and the energy distribution of each band could be calculated. Finally, these energy distributions could serve as the feature vectors, the grey relation degree of different vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can identify rotor fault patterns effectively.

Fuzzy grey relation analysis on torque influence factors of alternating current electrodyna-mometer

2013 ◽  
Vol 26 (8) ◽  
pp. 693-698 ◽  
Author(s):  
Zhigang Zhang ◽  
Guixiang Zhang ◽  
Teng Liu ◽  
Cheng Qian ◽  
Yuanwang Deng
Keyword(s):  
Influence Factors ◽  
Alternating Current ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Grey Relation
Sign in / Sign up

Export Citation Format

Share Document