Fault Prediction Model Based on Phase Space Reconstruction and Least Squares Support Vector Machines

Abstract

Combining phase space reconstruction theory and least squares support vector machines (LSSVM) method, a novel fault prediction model is proposed in this paper. The model reconstructs phase space for fault characteristics time series of the system and fit the nonlinear relationship of phase point evolution by use of least squares support vector machines according to the laws of phase space evolution. Fault prediction model based on gyroscope drift time series is established for single-step and multi-steps prediction compared with RBF neural network prediction results. The results show that phase space reconstruction method can effectively determine the input and output vectors of prediction model, and in the case of limited samples, the fault prediction model established by the least squares support vector machine has better accuracy and stronger generalization ability.