Abstract
The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions.
Highlights
An aero-engine is a kind of complex thermal mechanism, and its health states can affect the safe and stable flying of an aircraft [1]
In order to establish the aero-engine gas system health prediction model and consider the influence of expert knowledge and multiple working conditions, the working condition division model should be combined with the belief rule base (BRB) model to improve the accuracy of the gas system health prediction model
The health state pregas path system, The health state prediction results of BRB-1 and BRB-2 are taken as the input of BRB-3, diction model of can the predict gas path system shown in E.q which the healthisstates of the aero-engine gas path system
Summary
An aero-engine is a kind of complex thermal mechanism, and its health states can affect the safe and stable flying of an aircraft [1]. The literature [24] proposed a BP neural network based on phase space reconstruction under a single working condition, which integrates multiple health state features and improves the accuracy of aero-engine state prediction technology. This paper proposes an aero-engine gas path system health prediction model considering multiple working conditions. According to the result of the above, the BRB is used to fuse the health state monitoring data and prior expert knowledge to realize the health state prediction of the aero-engine gas path system considering multiple working conditions.
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