A hybrid ARIMA-SVM model for the study of the remaining useful life of aircraft engines

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:346

Article

Ordonez, Celestino¹  Sanchez Lasheras, Fernando²  Roca-Pardinas, Javier³  de Cos Juez, Francisco Javier¹ 

[1] Univ Oviedo, Dept Min Exploitat & Prospecting, C Independencia 13, Oviedo 33004, Spain

[2] Univ Oviedo, Dept Math, C Federico Garcia Lorca 18, Oviedo 33007, Spain

[3] Univ Vigo, Dept Stat & Operat Res, Vigo 32608, Spain

关键词: Remaining useful life (RUL);    Aircraft engines;    Vector autoregression moving-average (VARMA);    Support vector machines (SVM);    Genetic algorithms (GA);   

DOI  :  10.1016/j.cam.2018.07.008

来源: Elsevier

【 摘 要 】

In this research, an algorithm is presented for predicting the remaining useful life (RUL) of aircraft engines from a set of predictor variables measured by several sensors located in the engine. RUL prediction is essential for the safety of those aboard, but also to reduce engine maintenance and repair costs. The algorithm combines time series analysis methods to forecast the values of the predictor variables with machine learning techniques to predict RUL from those variables. First, an auto-regressive integrated moving average (ARIMA) model is used to estimate the values of the predictor variables in advance. Then, we use the result of the previous step as the input of a support vector regression model (SVM), where RUL is the response variable. The validity of the method was checked on an extensive public database, and the results compared with those obtained using a vector auto-regressive moving average (VARMA) model. Our algorithm showed a high prediction capability, far greater than that provided by the VARMA model. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free