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

Fengtao Wang, Shouhai Chen, Jian Sun, Dawen Yan, Lei Wang and Lihua Zhang

Mathematical Problems in Engineering, 2014, vol. 2014, 1-15

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:198362

DOI: 10.1155/2014/198362

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