基于贝叶斯网络的涡轴航空发动机性能优化策略  

Performance optimization scheme of turboshaft aeroengine based on Bayesian network

作　　者：王宁[1] 王宇航 蔡志强[2] 张帅[2] WANG Ning;WANG Yuhang;CAI Zhiqiang;ZHANG Shuai(College of Transportation Engineering, Chang′an University, Xi′an 710064, China;School of Mechanical Engineering, Northwestern Polytechnical University, Xi′an 710072, China)

机构地区：[1]长安大学运输工程学院,陕西西安710064 [2]西北工业大学机电学院,陕西西安710072

出　　处：《西北工业大学学报》2021年第2期375-381,共7页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金(71971030,71871181);中央高校基本科研业务费专项资金(300102220203,300102229304)资助。

摘　　要：涡轴航空发动机作为驱动旋翼产生升力和推进力的动力装置,主要应用在直升机上,近年来获得了迅速发展。涡轴发动机的生产过程复杂,有着严格的出厂检测机制,只有各项性能指标达到合格要求才能满足出厂条件,这使得涡轴发动机的出厂合格率往往不太理想。关键截面温度是表征涡轴发动机性能的一个重要指标,为保证整机的可靠性,其有着最高温度值的限制。结合制造商建议,提取出了影响发动机关键截面温度的4个属性变量,形成了研究数据集。对数据集进行预处理后,基于贝叶斯网络建立了涡轴发动机性能模型。根据贝叶斯网络的特性,通过性能模型概率推理进行后验合格概率的计算,并引入目前主流的机器学习算法对性能模型的有效性进行了对比验证。提出了推荐状态组合表,为涡轴航空发动机的性能优化提出有效建议。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.

关 键 词：贝叶斯网络 优化策略 涡轴发动机 性能优化 

分 类 号：TB114[理学—概率论与数理统计]