The aerocraft health diagnosis based on fuzzy C-means clustering analysis and acoustic emission technique

2008 ◽  
Author(s):  
Cui Jianguo ◽  
Xu Xinhe ◽  
Li Zhonghai ◽  
Zhang Daqian ◽  
Xu Changjun
Keyword(s):  
Acoustic Emission ◽  
Clustering Analysis ◽  
Acoustic Emission Technique ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Health Diagnosis

Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis

2013 ◽  
Vol 273 ◽  
pp. 409-413 ◽  
Author(s):  
Yu Xiang Cao ◽  
Xue Jun Li ◽  
Ling Li Jiang
Keyword(s):  
Time Domain ◽  
Clustering Analysis ◽  
Statistical Features ◽  
Fuzzy C Means ◽  
Time Domain Signal ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Fault Diagnostic ◽  
Characteristic Features ◽  
The Time Domain

For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

Tissue viability by multispectral near infrared imaging: a fuzzy C-means clustering analysis

1998 ◽  
Vol 17 (6) ◽  
pp. 1011-1018 ◽  
Author(s):  
J.R. Mansfield ◽  
M.G. Sowa ◽  
J.R. Payette ◽  
B. Abdulrauf ◽  
M.F. Stranc ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Tissue Viability ◽  
Near Infrared Imaging ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering
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