Based on semi-fuzzy c-means clustering BIT fault diagnosis
Xiaotian Yang, Jinfu Feng, Jinlin Wang, Yuan Feng · 2012
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.