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International Journal of Rotating Machinery
Volume 2016 (2016), Article ID 5980802, 7 pages
Research Article

Full-Vector Signal Acquisition and Information Fusion for the Fault Prediction

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

Received 21 September 2015; Revised 6 January 2016; Accepted 28 February 2016

Academic Editor: Robert C. Hendricks

Copyright © 2016 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.


Fault prediction is the key technology of the predictive maintenance. Currently, researches on fault prediction are mainly focused on the evaluation of the intensities of the failure and the remaining life of the machine. There is lack of methods on the prediction of fault locations and fault characters. To satisfy the requirement of the prediction of the fault characters, the data acquisition and fusion strategies were studied. Firstly, the traditional vibration measurement mechanism and its disadvantages were presented. Then, the full-vector data acquisition and fusion model were proposed. After that, the sampling procedure and information fusion algorithm were analyzed. At last, the fault prediction method based on full-vector spectrum was proposed. The methodology is that of Dr. Bently and Dr. Muszynska. On the basis of this methodology, the application study has been carried out. The uncertainty of the spectrum structure can be eliminated by the designed data acquisition and fusion method. The reliability of the diagnosis on fault character was improved. The study on full-vector data acquisition system laid the technical foundation for the prediction and diagnosis research of the fault characters.