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

Abstract EN

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.