Research Article
Time-Frequency Fault Feature Extraction for Rolling Bearing Based on the Tensor Manifold Method
Table 3
Classification results of the inner-race fault using the PNN with the proposed tensor manifold features.
| Sample |
Category vector of the normal-state samples |
Category vector of the inner-race mild-damage fault samples |
Category vector of the inner-race moderate-damage fault samples |
Category vector of the inner-race severe-damage fault samples |
| 1 | 1 | 0 | 0 | 0 | 0 | 0.97 | 0.01 | 0.02 | 0 | 0.01 | 0.98 | 0.01 | 0 | 0.01 | 0.02 | 0.97 | 2 | 1 | 0 | 0 | 0 | 0 | 0.96 | 0.02 | 0.02 | 0 | 0.02 | 0.97 | 0.01 | 0 | 0.02 | 0.02 | 0.96 | 3 | 1 | 0 | 0 | 0 | 0 | 0.98 | 0.01 | 0.01 | 0 | 0.01 | 0.97 | 0.02 | 0 | 0.03 | 0.02 | 0.95 | 4 | 1 | 0 | 0 | 0 | 0 | 0.95 | 0.02 | 0.03 | 0 | 0.03 | 0.95 | 0.02 | 0 | 0.02 | 0.02 | 0.96 | 5 | 1 | 0 | 0 | 0 | 0 | 0.96 | 0.02 | 0.02 | 0 | 0.01 | 0.98 | 0.01 | 0 | 0.01 | 0.03 | 0.96 | 6 | 1 | 0 | 0 | 0 | 0 | 0.97 | 0.01 | 0.02 | 0 | 0.01 | 0.96 | 0.03 | 0 | 0.05 | 0.02 | 0.93 | 7 | 1 | 0 | 0 | 0 | 0 | 0.94 | 0.05 | 0.02 | 0 | 0.02 | 0.95 | 0.03 | 0 | 0.03 | 0.02 | 0.95 | 8 | 1 | 0 | 0 | 0 | 0 | 0.95 | 0.03 | 0.02 | 0 | 0.02 | 0.97 | 0.01 | 0 | 0.01 | 0.01 | 0.98 | 9 | 1 | 0 | 0 | 0 | 0 | 0.94 | 0.02 | 0.04 | 0 | 0.04 | 0.94 | 0.02 | 0 | 0.03 | 0.03 | 0.94 | 10 | 1 | 0 | 0 | 0 | 0 | 0.93 | 0.02 | 0.05 | 0 | 0.04 | 0.93 | 0.03 | 0 | 0.03 | 0.01 | 0.96 |
|
|