Open AccessPrediction for civil aero-engine performance after shop visit based on lazy support vector machine regression model
Author(s) -
Xuyun Fu,
Xingjie Zhou,
Shisheng Zhong
Publication year - 2020
Publication title -
journal of prognostics and health management
Language(s) - English
Resource type - Journals
ISSN - 2563-6685
DOI - 10.22215/jphm.v1i1.1336
Subject(s) - support vector machine , computer science , sample (material) , aero engine , regression , machine learning , function (biology) , artificial intelligence , process (computing) , regression analysis , scope (computer science) , relevance vector machine , data mining , engineering , mathematics , statistics , mechanical engineering , programming language , operating system , chemistry , chromatography , evolutionary biology , biology
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.