A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines
By C. Ordóñez, F. Sánchez Lasheras, Javier Roca-Pardiñas, Francisco Javier de Cos Juez. Journal of Computational and Applied Mathematics, 2019
205 citations recorded by the source corpus.
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.
Subfields: Control and Systems Engineering, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/f85fb3d88bc3843e19a13f1e10956ceb5abd273a
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In Control and Systems Engineering
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Citations in the corpus
- Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis (2020, methodology): “Several hybrid models based on ARIMA and neural networks are available in the field of time series forecasting; see for example [35; 1; 12; 19; 8; 25].”