A method of multifeatures fusion based on normalized Euclidean distance was proposed to meet the requirement of monitoring aeroengine rolling bearing real time condition. Firstly

the bearing fault simulation experiments were carried out while the fault sensitivity of the features before and after fusion was analysed by introducing a quantitative evaluation index of fault sensitivity. Then

the proposed method was compared with the principal component analysis

support vector data description and support vector distribution estimation. Finally

the bearing fatigue accelerated experiment was carried out

and the proposed fusion method was applied to the aeroengine rolling bearing condition monitoring. Experimental results show that compared with principal component analysis

support vector data description and support vector distribution estimation

the fault sensitivity of fusion value based on normalized Euclidean distance is higher. For different types and different stages of the bearing fault

the fusion value obtained by proposed method is more sensitive

which is more suitable to be an index of the bearing condition monitoring

compared with the effective value.