Abstract: As the core component of a loader, the loader boom's fatigue life and reliability directly affect the performance and safety of the whole machine. For large structural components, the test cost is extremely high and available samples are limited, so traditional reliability evaluation methods are often difficult to apply directly. Based on fatigue test data of the loader working device, this paper reviews three reliability evaluation methods for loader working devices under minimum sample size conditions: the virtual sample augmentation combined with modified Bootstrap method, the GM grey model combined with Bootstrap method, and the BP neural network combined with Bootstrap method. By analyzing the characteristics and applicability of the three methods, it is expected to open up new approaches for reliability evaluation of costly large structural components.