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Mathematical Problems in Engineering
Volume 2014, Article ID 198362, 15 pages
http://dx.doi.org/10.1155/2014/198362
Research Article

Time-Frequency Fault Feature Extraction for Rolling Bearing Based on the Tensor Manifold Method

1Institute of Vibration Engineering, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
2School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
3Institute of Microelectromechanical Systems and Precision Engineering, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China

Received 2 May 2014; Revised 20 June 2014; Accepted 10 July 2014; Published 4 August 2014

Academic Editor: Weihua Li

Copyright © 2014 Fengtao Wang 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

Rolling-bearing faults can be effectively reflected using time-frequency characteristics. However, there are inevitable interference and redundancy components in the conventional time-frequency characteristics. Therefore, it is critical to extract the sensitive parameters that reflect the rolling-bearing state from the time-frequency characteristics to accurately classify rolling-bearing faults. Thus, a new tensor manifold method is proposed. First, we apply the Hilbert-Huang transform (HHT) to rolling-bearing vibration signals to obtain the HHT time-frequency spectrum, which can be transformed into the HHT time-frequency energy histogram. Then, the tensor manifold time-frequency energy histogram is extracted from the traditional HHT time-frequency spectrum using the tensor manifold method. Five time-frequency characteristic parameters are defined to quantitatively depict the failure characteristics. Finally, the tensor manifold time-frequency characteristic parameters and probabilistic neural network (PNN) are combined to effectively classify the rolling-bearing failure samples. Engineering data are used to validate the proposed method. Compared with traditional HHT time-frequency characteristic parameters, the information redundancy of the time-frequency characteristics is greatly reduced using the tensor manifold time-frequency characteristic parameters and different rolling-bearing fault states are more effectively distinguished when combined with the PNN.