The turboshaft aeroengine is mainly used in helicopters. As a power device that drives the rotor to generate lift and propulsion, it has been rapidly developed in recent years. The manufacturing process of turboshaft aeroengine is complex, and there is a strict factory inspection mechanism. Only when the various performance indicators meet the qualified requirements of the factory conditions, it makes the ex factory pass rate of turboshaft aeroengine often not ideal. The key section temperature is an important indicator to characterize the performance of turboshaft aeroengine. In order to ensure the reliability of the whole machine, it has a maximum temperature limit. According to the manufacturer's suggestions, four attribute variables that affect the key section temperature are extracted to form a research data set. Then, after preprocessing the data set, the performance model for the turboshaft aeroengine is established based on the Bayesian network. According to the characteristics of Bayesian network, the posterior qualified probability is calculated through probabilistic reasoning of the performance model, and the current mainstream machine learning algorithms are introduced to compare and verify the validity of the performance model. Finally, the recommended state combination table is proposed, which provides the effective suggestions for the performance optimization of turboshaft aeroengine.
Performance optimization scheme of turboshaft aeroengine based on Bayesian network
