Most reliability models in the classical reliability theory use probability theory to quantify information under uncertainty and these models have a high demand of available data. While in practical applications, data related to system sand components are usually sparse or incomplete, moreover, some quantification of uncertainty should be based on subjective information and moreover subjective information may often be expressed in natural language. In these cases, classical reliability theory can not provide an effective way for the reliability analysis of systems. As a generalization of probability theory, imprecise probability theory may provide a tool which is called the natural extension to solve such problems. Research on the application of imprecise probability in reliability engineering has made a great progress. Unfortunately, this is all about theory and there are few applications and cases in practice. Therefore, this paper tried to overcome the flaws and shortcomings of classical reliability theory, and apply some theories of imprecise probability in practical engineering, and conduct reliability analysis on automotive semi-axle with establishing imprecise probability-based reliability model of automotive semi-axle. Natural extension is undoubtedly a powerful tool to compute reliability measures, however, when number of variables is large; the computation of natural extension is very difficult. Effective methods to simplify and build reliability models deserve more debate in the future.
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