Predicting engine reliability by support vector machines

Capturing the trends of engine failure data and predicting system reliability are very essential issues in engine manufacturing. The support vector machines (SVMs) have been successfully applied in solving nonlinear regression and times series problems. However, the application of SVMs to reliability forecasting is not widely explored. Therefore, to aim at examining the feasibility of SVMs in reliability predicting, this study is a first attempt to apply a SVM model to predict engine reliability. In addition, three other time series forecasting approaches, namely the Duane model, the autoregressive integrated moving average (ARIMA) time series model and general regression neural networks (GRNN), are used to compare the predicting performance. The experimental results show that the SVM model is a valid and promising alternative in reliability prediction.