Xia Xin-Tao
College of Mechatronical Engineering, Henan University of Science and Technology, Luoyang 471003, China
Meng Yan-Yan
College of Mechatronical Engineering, Henan University of Science and Technology, Luoyang 471003, China
Qiu Ming
College of Mechatronical Engineering, Henan University of Science and Technology, Luoyang 471003, China
ABSTRACT
A dynamical Bayesian testing method is proposed to examine feature information on performance variation of time series with poor information in advance. Sub-series of time series are obtained via a regularly sampling, a multidimensional information space is formed by phase-space reconstruction method, probability density functions of phase trajectories are acquired with bootstrap and maximum entropy theory, a referenced sequence from phase trajectories is found by minimum variance principle, the posterior probability density function is established according to Bayesian theory and the mutation probability is defined in the light of fuzzy set theory. At the given significance level, dynamical Bayesian testing for feature information on performance variation of the poor information process is put into effect with the help of the mutation probability. Experimental investigation on vibration acceleration of a rolling bearing for space applications presents that the method proposed can effectively detect feature information on performance variation of time series with the unknown probability distribution and trend for the early detection of the hidden danger, thus avoiding serious accident.
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How to cite this article
Xia Xin-Tao, Meng Yan-Yan and Qiu Ming, 2013. Dynamical Bayesian Testing for Feature Information of Time Series with Poor
Information using Phase-space Reconstruction Theory. Information Technology Journal, 12: 5713-5718.
DOI: 10.3923/itj.2013.5713.5718
URL: https://scialert.net/abstract/?doi=itj.2013.5713.5718
DOI: 10.3923/itj.2013.5713.5718
URL: https://scialert.net/abstract/?doi=itj.2013.5713.5718
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