LASSO based variable selection for similarity remaining useful life prediction of aero-engine

被引：0

作者：

Yu Q. ^{[1
]}

Li J. ^{[1
]}

Dai H. ^{[2
]}

Xin F. ^{[1
]}

机构：

[1] School of Mathematics and Statistics, Ludong University, Shandong, Yantai

[2] School of Basic Sciences for Aviation, Naval Aviation University, Shandong, Yantai

来源：

Hangkong Dongli Xuebao/Journal of Aerospace Power | 2023年 / 38卷 / 04期

关键词：

K-means clustering; LASSO method; prognostics health management; remaining useful life; similarity;

D O I：

10.13224/j.cnki.jasp.20210516

中图分类号：

学科分类号：

摘要：

Due to the large number of aero-engine monitoring variables, the variables with obvious performance degradation trend were directly selected by traditional method for the life prediction, so a variable selection method based on LASSO (least absolute shrinkage and selection operator) was proposed, which combined with the similarity life prediction method to effectively improve the prediction accuracy. Based on K-means clustering, different working conditions were distinguished, and multiple monitoring variables of aero-engine were transformed according to the clustering results. The optimal sensor variables were selected based on the LASSO method. The remaining useful life of aero-engine was predicted based on similarity method. The results of remaining useful life prediction based on the variable selection method by LASSO and the traditional selection method by the degradation trend were compared. The results showed that the standard deviation of the similarity life prediction error based on the variables selected by LASSO decreased by about 1.84, 3.46 and 4.23 under three operating cycles. © 2023 BUAA Press. All rights reserved.

引用

页码：931 / 938

页数：7

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