To solve the problems of large computation and low precision during gear vibration reliability analysis

a reliability analysis method based on dimensionality reduction visualization and Kriging was proposed. Sample points were generated by Monte Carlo method. These points were transformed into two-dimensional pole feature space

and then Kriging model was used to predict the dividing line of safe and failure regions. When predicting the dividing line

an active learning approach of selecting points was introduced to establish Kriging model so that the utilization rate of sample points was improved dramatically

thanks to the properties of nonlinear prediction and error estimation of Kriging. Through gear vibration reliability analysis

and by comparing with traditional dimensionality reduction visualization technique

it is shown that the number of calls to the performance function changes from 975 numbers to 149 numbers

and calculation time changes from 12400s to 1810s. What's more

the result of this method is consistent with that of 100000 Monte Carlo simulation

so the efficiency and correctness is validated.