Title :
Based on semi-fuzzy c-means clustering BIT fault diagnosis
Author :
Yang, Xiaotian ; Feng, Jinfu ; Wang, Jinlin ; Feng, Yuan
Author_Institution :
Mil. Representative in Seven Six Nine Factory, Chinese People´´s Liberation Army, Baoji, China
Abstract :
A semi-fuzzy c-means algorithm based on revised Euclidean distance was proposed to improve the real-time capability and precision in fault pattern classified. Effectiveness of threshold parameters on clustering was investigated, and then program steps were given. The example of fault diagnosis in an airborne fire control system BIT was developed. The results show that the new algorithm can recognize fault pattern adaptively and precisely.
Keywords :
aerospace control; aerospace testing; built-in self test; fault diagnosis; fuzzy set theory; pattern classification; pattern clustering; Euclidean distance; airborne fire control system BIT; fault pattern classification; semifuzzy C-mean clustering BIT fault diagnosis; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Control systems; Euclidean distance; Fault diagnosis; Partitioning algorithms; BIT; Clustering Algorithm; Semi-Fuzzy c-Means;
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4577-1414-6
DOI :
10.1109/CECNet.2012.6202146
