Research on Diagnosis Data Fusion of Aero-engine based on Improved K-means Cluster and D-S Evidence Theory

摘要

The data fusion method combined improved K-means clustering algorithm with D-S evidence theory was used for vibration fault data fusion of aero-engine in this paper. The later calculated amount was reduced by the improved K-means clustering algorithm. On the basis of the improved K-means clustering algorithm, the basic belief function of vibration data was determined. D-S evidence theory was used for fusion of fault vibration data of aero-engine which had been processed through the improved K-means clustering analysis. The results of diagnostic instance show that the method can improve the diagnosis rates of aero-engine fault effectively.