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

Content Provider	IEEE Xplore Digital Library
Author	Yunhong Gao Yibo Li
Copyright Year	2009
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.
Starting Page	464
Ending Page	467
File Size	268994
Page Count	4
File Format	PDF
ISBN	9780769537450
DOI	10.1109/HIS.2009.307
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automation phase space reconstruction Prediction methods Predictive models Educational institutions Least squares methods Support vector machines fault prediction model least squares support vector machines Neural networks Hidden Markov models Gyroscopes gyroscope drift Mathematical model
Content Type	Text
Resource Type	Article

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