基于集成双粒子滤波器的燃气涡轮发动机预测方法研究

Prognosis of Gas Turbine Engines Using Ensemble Dual Particle Filters

燃气涡轮发动机中关键部件潜在故障的自动检测、隔离和预测是飞机维修人员关注的问题。自动监测设备有助于大幅降低维修和更换损伤部件的成本,甚至可能挽救飞行员生命。本文利用双粒子滤波器和集成学习方法,提出了一种用于燃气涡轮发动机预测的混合架构。在本文提出的框架中,双粒子滤波器用于估计燃气涡轮发动机系统的状态和健康参数,并开发了一种基于长短期记忆神经网络的测量值预测方案,将系统的测量值扩展到未来时间范围。此外,通过开发多个预测器,利用每个预测器具有的不同属性,实现根据不同的可能场景进行的多重估计。在此基础上,基于集成学习方法提出了一种数据融合架构,根据剩余寿命预测中的不确定性对燃气涡轮发动机的影响计算各预测器的权重。通过将本文提出的集成预测方法应用于双轴喷气发动机的健康状况监测对其进行验证,与现有单预测器方法相比,本文开发的集成架构为燃气涡轮发动机的退化趋势预测提供了改进的决策支持。

A problem of interest to aircraft maintainers is automatic detection,classification,and prognosis of potential critical component failures in the gas turbine engines.Automatic monitoring offers the promise of substantially reducing the cost of repair and replacement of defective parts and may even result in saving lives.In this paper,an integration architecture is proposed for gas turbine engine prognosis through utilization of dual particle filters and ensemble learning methods.In our proposed framework,dual particle filters are utilized to estimate the states as well as the health parameters of the gas turbine engine system.An Long-short term memory neural network based observation forecasting scheme is developed to extend the system observation profiles to future time horizon.Moreover,we develop multiple predictors,each with differing properties so that multiple estimates can be made based on different possible scenarios.A data fusion architecture is presented that combines multiple prognostic techniques weighted by their individual prediction uncertainty for the remaining useful life(RUL)of a gas turbine engine asset.The architecture is based on the concept of ensemble learning method.To verify and validate the above results and as a case study,our proposed ensemble approach is applied to predict the health condition of a dual spool jet engine.The developed ensemble architecture provides improved decision support in comparison to the existing single prognostic technique methods that are available in the literature.