Prediction for civil aero-engine performance after shop visit based on lazy support vector machine regression model

Xuyun Fu,Shisheng Zhong,Xingjie Zhou

doi:10.22215/jphm.v1i1.1336

Abstract

Through consideration of problems that the influence of the aero-engine state before shop visit and the adopted maintenance work scope on its performance after shop visit is complex and the sample size is small, we propose a lazy support vector machine regression (LSVMR) model for aero-engine performance prediction after shop visit based on the ε-support vector machine regression (ε-SVMR) model. Unlike the ε-SVMR, the insensitive loss function in LSVMR depends on the distance between the training sample and the predicted sample. The proposed model not only makes full use of the information of the predicted sample, but also seeks the best tradeoff between the model complexity and the learning ability. In this article, we give the solving process of LSVMR and collect the actual aero-engine maintenance samples from an airline to validate it. By comparing the prediction accuracy among LSVMR, ε-SVMR and k-nearest neighbor algorithm (k-NN), we find that LSVMR has the best prediction accuracy and can be seen as an effective method for the aero-engine performance prediction after shop visit.

Highlights

The aero-engine is an important core component of the aircraft
We combine support vector machine regression (SVMR) and the lazy prediction method and propose the lazy SVMR (LSVMR) model, which is suitable for the aero-engine performance prediction after shop visit
We find that lazy support vector machine regression (LSVMR) has the best prediction accuracy after experiments and determination of the aero-engine maintenance work scope can be supported

Summary

Introduction

The aero-engine is an important core component of the aircraft. In order to ensure flight safety, the aero-engine must be sent to the workshop for maintenance when the airworthiness requirements are not met. It is difficult to directly propose a physical model to predict the aero-engine performance after shop visit, due to the complex influence of the aero-engine state before shop visit and the adopted maintenance work scope on its performance after shop visit For these reasons, data-driven prediction methods are generally adopted. We combine SVMR and the lazy prediction method and propose the lazy SVMR (LSVMR) model, which is suitable for the aero-engine performance prediction after shop visit. By deducing the LSVMR algorithm, the decision function and optimal solution of the model can be obtained We apply this proposed model to the actual aero-engine performance prediction after shop visit and compare the prediction accuracy among LSVMR, ε-SVMR and k-NN. In contrast with Eq (3) we can find that the LSVMR is exactly the same as ε-SVMR except for insensitive loss function

Model Solution

Application Case

Training

Conclusions