A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines
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 re...
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| Published in | Journal of computational and applied mathematics Vol. 346; pp. 184 - 191 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.01.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0377-0427 1879-1778 1879-1778 |
| DOI | 10.1016/j.cam.2018.07.008 |
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| Abstract | 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.
•A method to forecast the remaining useful life of aircraft engines is proposed.•The predictor variables were obtained from sensors located in the engine.•The proposed method combines ARIMA and SVM models.•Results of our method unsurpassed those obtained using a VARMA model. |
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| AbstractList | 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.
•A method to forecast the remaining useful life of aircraft engines is proposed.•The predictor variables were obtained from sensors located in the engine.•The proposed method combines ARIMA and SVM models.•Results of our method unsurpassed those obtained using a VARMA model. |
| Author | Roca-Pardiñas, Javier Juez, Francisco Javier de Cos Ordóñez, Celestino Sánchez Lasheras, Fernando |
| Author_xml | – sequence: 1 givenname: Celestino surname: Ordóñez fullname: Ordóñez, Celestino organization: Department of Mining Exploitation and Prospecting, University of Oviedo, c/Independencia 13, 33004 Oviedo, Spain – sequence: 2 givenname: Fernando orcidid: 0000-0002-7052-2811 surname: Sánchez Lasheras fullname: Sánchez Lasheras, Fernando email: sanchezfernando@uniovi.es organization: Department of Mathematics, University of Oviedo, c/Federico García Lorca 18, 33007 Oviedo, Spain – sequence: 3 givenname: Javier surname: Roca-Pardiñas fullname: Roca-Pardiñas, Javier organization: Department of Statistics and Operations Research, University of Vigo, 32608 Vigo, Spain – sequence: 4 givenname: Francisco Javier de Cos surname: Juez fullname: Juez, Francisco Javier de Cos organization: Department of Mining Exploitation and Prospecting, University of Oviedo, c/Independencia 13, 33004 Oviedo, Spain |
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| Keywords | Remaining useful life (RUL) Genetic algorithms (GA) Aircraft engines Vector autoregression moving-average (VARMA) Support vector machines (SVM) |
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| SubjectTerms | Aircraft engines Genetic algorithms (GA) Remaining useful life (RUL) Support vector machines (SVM) Vector autoregression moving-average (VARMA) |
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| Title | A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines |
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