Active control of aero-engine turbine tip clearance is one of the best chances for engine performance uplift currently.To do that,the first requirement is real-time measurement of tip clearance in aero-engine working ...Active control of aero-engine turbine tip clearance is one of the best chances for engine performance uplift currently.To do that,the first requirement is real-time measurement of tip clearance in aero-engine working environment.However,turbine complexity makes it unlikely for tip clearance sensors to be loaded.In recognition of that,this paper proposed a model-based method for tip clearance measurement.Firstly,by considering previously wrongly neglected factors such as load deformation,a mathematical model to monitor dynamic tip clearance changes is built to improve calculation accuracy.Then,after clarifying the coupling relationship between engine models and tip clearance models,this paper builds a component-level mathematical model integrating dynamic characteristics of turbine tip clearance,which helps realize accurate measurement of tip clearance in working environment.How tip clearance affects turbine efficiency is studied afterwards and reported to aero-engine model,so as to mitigate performance difference between aero-engine model and real engines caused by turbine tip clearance.Lastly,by hardware-in-the-loop simulation,tip clearance model demonstrates 15.9%better accuracy than previously built models in terms of turbine centrifugal deformation calculation.As tip clearance measurement model takes averagely 0.34 ms in calculation,meeting the operation requirement,it proves to be an effective new way.展开更多
The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas...The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51906103,52176009).
文摘Active control of aero-engine turbine tip clearance is one of the best chances for engine performance uplift currently.To do that,the first requirement is real-time measurement of tip clearance in aero-engine working environment.However,turbine complexity makes it unlikely for tip clearance sensors to be loaded.In recognition of that,this paper proposed a model-based method for tip clearance measurement.Firstly,by considering previously wrongly neglected factors such as load deformation,a mathematical model to monitor dynamic tip clearance changes is built to improve calculation accuracy.Then,after clarifying the coupling relationship between engine models and tip clearance models,this paper builds a component-level mathematical model integrating dynamic characteristics of turbine tip clearance,which helps realize accurate measurement of tip clearance in working environment.How tip clearance affects turbine efficiency is studied afterwards and reported to aero-engine model,so as to mitigate performance difference between aero-engine model and real engines caused by turbine tip clearance.Lastly,by hardware-in-the-loop simulation,tip clearance model demonstrates 15.9%better accuracy than previously built models in terms of turbine centrifugal deformation calculation.As tip clearance measurement model takes averagely 0.34 ms in calculation,meeting the operation requirement,it proves to be an effective new way.
基金supported by the Shandong Provincial Natural Science Foundation(No.ZR2019MEE096)the Key National Natural Science Foundation of China(No.U1733201)。
文摘The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.
