A new rotor fault identification method based on normalized energy distribution and grey relation degree

Author(s):  
Wenbin Zhang ◽  
Yanping Su ◽  
Yasong Pu ◽  
Yanjie Zhou ◽  
Ruijing Teng
2013 ◽  
Vol 684 ◽  
pp. 373-376
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Ya Song Pu ◽  
Yan Jie Zhou

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.


2021 ◽  
Vol 60 (4) ◽  
pp. 4047-4056
Author(s):  
Erbao Xu ◽  
Yan Li ◽  
Lining Peng ◽  
Mingshun Yang ◽  
Yong Liu

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 229
Author(s):  
Wende Tian ◽  
Shifa Zhang ◽  
Zhe Cui ◽  
Zijian Liu ◽  
Shaochen Wang ◽  
...  

Due to the complexity of materials and energy cycles, the distillation system has numerous working conditions difficult to troubleshoot in time. To address the problem, a novel DMA-SDG fault identification method that combines dynamic mechanism analysis based on process simulation and signed directed graph is proposed for the distillation process. Firstly, dynamic simulation is employed to build a mechanism model to provide the potential relationships between variables. Secondly, sensitivity analysis and dynamic mechanism analysis in process simulation are introduced to the SDG model to improve the completeness of this model based on expert knowledge. Finally, a quantitative analysis based on complex network theory is used to select the most important nodes in SDG model for identifying the severe malfunctions. The application of DMA-SDG method in a benzene-toluene-xylene (BTX) hydrogenation prefractionation system shows sound fault identification performance.


2012 ◽  
Vol 433-440 ◽  
pp. 2611-2618
Author(s):  
Zhen Hua Tian ◽  
Hong Yuan Li ◽  
Hong Xu

The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.


2013 ◽  
Vol 694-697 ◽  
pp. 1155-1159
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Yan Jie Zhou ◽  
Ya Song Pu

In this paper, a novel intelligent method to identify gear fault pattern was approached based on morphological filter, harmonic wavelet package and grey incidence. At first, the line structure element was selected for morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and energy distribution of each band was calculated. Finally, these energy distributions could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.


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