Most of the existing researches on belief rule-base inference methodology focus on the problem of parameter optimization

while few on the problem that the belief rule-base system cannot operate normally due to the incomplete input information caused by the difficulty of data acquisition. In order to solve this problem

an inference method based on incomplete input is proposed. First

the precondition attributes distribution was obtained from experience or historical data using statistical method. Then

the step sampling method was used to obtain multiple candidate values for the missing precondition attribute

which was combined with other precondition attributes. Each of the inputs was inferred using the belief rule-base inference methodology. Finally

the ER algorithm was used to fuse all the input inference results. In the simulation experiment of automobile engine fault diagnosis

the method of this paper was compared with other methods. Results show that the cumulative inference error of the proposed method was obviously smaller than other methods when there was sufficient historical data. Moreover

through multiple experiments

it was found that the proposed method has good adaptability to different distributions. This method reduces the inference error on the basis of minimizing the computational complexity

and provides a new idea for the belief rule-base inference of incomplete input information.