The wear reliability assessment plays a critical role in evaluating product quality for piston ring-cylinder liner (PRCL) components. In practice, the wear data of components is discrete and sparse, which makes it difficult to evaluate the wear reliability of PRCL components. However, the sample's wear data can effectively compensate for the shortcomings of the component's wear data. A pressing issue in engineering applications is how to effectively assess the wear reliability of PRCL components by making the most of the wear data of samples and components and quantifying the links between samples and components. Hence, this paper proposes an approach to wear reliability assessment for PRCL components based on probability mapping. First, a wear reliability model for PRCL samples and components is created using the improved Rhee wear model, acceleration model, and wear distribution characteristics. The issue of inadequate wear data is resolved by estimating model parameters using the Bayesian approach. The component's wear reliability model is then optimized after the degree to which temperature, load, and time affect the quantity of wear is examined using the sensitivity analysis approach. Second, a probability mapping approach is proposed to consider three mapping relationships. The ratio of the characteristic values (mean and standard deviation) of the wear reliability model parameters of the samples and components represents the mapping connection between PRCL samples and components. Lastly, the sample wear reliability model and mapping relationship are used to forecast the components' wear depth, wear life, and wear reliability, which are then compared to the experimental findings. Compared to mean mapping and no mapping, mean and standard deviation mapping predicted a decrease in the percentage error of mean wear life for piston ring components by 5.57% and 14.78%, and cylinder liner components by 4.81% and 9.13%, respectively. The wear reliability of PRCL components under the average wear amount is 0.51, which is closest to the true value of 0.5. Under the assessment test conditions, the predicted wear reliability of PRCL components based on mean and standard deviation mapping at 832.55h are 0.8986 and 0.8224, respectively, which are closer to the test result of 0.8333. The research results indicate that this approach can forecast PRCL components' wear depth, wear life, and wear reliability with rapidity, precision, and effectiveness. This approach can serve as a guide for routine maintenance and quality evaluation of piston ring cylinder liner components.