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Shock and Vibration
Volume 2017, Article ID 6103947, 8 pages
https://doi.org/10.1155/2017/6103947
Research Article

Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

1Institute of Vibration Engineering, Zhengzhou University, Zhengzhou 450001, China
2School of Chemical Engineering and Energy, Zhengzhou University, Zhengzhou 450001, China

Correspondence should be addressed to Lei Chen; nc.ude.uzz@ielnehc

Received 6 October 2016; Revised 6 February 2017; Accepted 21 February 2017; Published 12 March 2017

Academic Editor: Hassan Askari

Copyright © 2017 Lei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steam turbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of the machine fault with only one transducer may result in a fault judgement with partiality, an information fusion method based on the theory of full vector spectrum was adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.