关键词: 经验模态分解, 最小二乘支持向量机, 时间序列预测, 时间序列分析, 故障率预测

Abstract: A prediction method based on support vector empirical mode decomposition (SVEMD) is proposed to deal with the non-linearity and non-stationarity of failure rate data. First, the failure rate data is decomposed into a series of intrinsic mode functions (IMFs) and a residual function (RF) by using empirical mode decomposition (EMD), and then a least squares support vector machine (LSSVM) is used to predict the local extremal points of the failure rate data and solve the end effect problem of the EMD. The upper and lower envelopes are constructed by using LSSVM regression instead of spline interpolation in EMD. Machine-learning-based prediction models are trained to predict the IMFs and RF. Finally, the prediction results of the failure rate data are obtained by integrating the prediction results of the IMFs and RF. Experiments on a plane failure rate prediction indicate that the proposed SVEMD-based prediction method can predict failure rate data accurately and has better performance in prediction accuracy than the traditional EMD-based prediction methods.

Key words: empirical mode decomposition, least squares support vector machine, time series prediction, time series analysis, failure rate prediction