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Shock and Vibration
Volume 2018 (2018), Article ID 9067184, 15 pages
Research Article

An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics

1School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
2Sichuan Special Equipment Inspection and Research Institute, Chengdu 610051, China

Correspondence should be addressed to Lixiang Duan

Received 4 August 2017; Revised 21 December 2017; Accepted 15 January 2018; Published 18 February 2018

Academic Editor: Rafał Burdzik

Copyright © 2018 Lixiang Duan 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.


Aimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a vibration signal to their respective cumulative forms. The technique not only makes the extracted features show a monotonic trend but also reduces the fluctuation; such properties are more propitious to reflect the bearing degradation trend. Then, a new degradation index system is constructed, which fuses multidimensional cumulative features by kernel principal component analysis (KPCA). Finally, an extreme learning machine model based on phase space reconstruction is proposed to predict the degradation trend. The model performance is experimentally validated with a whole-life experiment of a rolling bearing. The results prove that the proposed method reflects the bearing degradation process clearly and achieves a good balance between model accuracy and complexity.