This paper focuses on the challenge of predicting the components and full-engine reliability of automotive diesel engines and proposes an integrated approach combining mechanistic modeling with statistical data. The mechanism model was developed for engine performance, dynamics, as well as wear and fatigue damage of critical components. Uncertain parameters were processed using Monte Carlo and Latin hypercube sampling methods to compute damage distributions and reliability curves for both components and full-engine system. Using a specific automotive diesel engine as a case study, the wear model of the main bearing shell was verified on the reliability test bench, with an error of only 2.03%. Damage calculations were conducted on several critical components, the results demonstrate that damage to the components conforms to the cumulative damage phenomenon, moreover, the damage distribution for the piston ring and exhaust valve exhibits an increasing dispersion under the effects of stochastic influences. The reliability curves align with the bathtub curve . The mechanism model yields a full-engine MTBF of 7091 h (242,000 km), a B10 life of 6050 h (206,000 km), and a system-wide MTBF of 1490 h (50,000 km) after integrating subsystem failure rates. This methodology offers effective tools for engine reliability design and predictive maintenance.
Jiang et al. (Sun,) studied this question.