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In order to address the characteristics of the oil system that is difficult to reach and has high importance in daily maintenance, a test analysis of the oil system based on a simulation model was conducted to optimize the diagnostic strategy of the oil system. Based on the working principle of the full-flow single-loop reverse circulation oil system, a virtual simulation model of the oil system was established using AMESim software, and the accuracy of the built AMESim model was verified by using Simulink model simulation results, with an error within 3.5%. A Bayesian network of basic fault events of the oil system was established to analyze the important fault modes of the oil system, which was used to determine important fault events of the oil system as a basis for conducting fault injection research based on simulation models. Fault injection was implemented in the model, and by analyzing the changes in preset test point parameters, a fault-test correlation matrix was obtained to achieve testability analysis and optimization of the oil system. After optimization, the fault detection rate increased by 16.7% and the fault isolation rate increased by 40.0%. The results of testability analysis can be used to optimize the sensor layout design of the oil system and provide support for fault diagnosis of the oil system.