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

Table 1

Classification results of the four bearing faults using the PNN with the proposed tensor manifold features.

Sample	Category vector of the normal-state samples				Category vector of the inner-race fault samples				Category vector of the; ball fault samples				Category vector of the outer-race fault samples
1	1	0	0	0	0	0.97	0.01	0.02	0	0.01	0.99	0	0	0.01	0.02	0.97
2	1	0	0	0	0	0.99	0	0.01	0	0.02	0.97	0.01	0	0.05	0.03	0.92
3	1	0	0	0	0	0.95	0.03	0.02	0	0.02	0.97	0.01	0	0.03	0.02	0.95
4	1	0	0	0	0	0.98	0.01	0.01	0	0.03	0.95	0.02	0	0.02	0.05	0.93
5	1	0	0	0	0	0.93	0.05	0.02	0	0.02	0.98	0	0	0.02	0.02	0.96
6	1	0	0	0	0	0.97	0.02	0.01	0	0.01	0.96	0.03	0	0.05	0.03	0.92
7	1	0	0	0	0	0.95	0.02	0.03	0	0.02	0.97	0.01	0	0.02	0.01	0.97
8	1	0	0	0	0	0.92	0.03	0.05	0	0.03	0.93	0.04	0	0.04	0.03	0.93
9	1	0	0	0	0	0.94	0.02	0.04	0	0.02	0.95	0.03	0	0.03	0.02	0.95
10	1	0	0	0	0	0.96	0.02	0.02	0	0.01	0.97	0.02	0	0.01	0.01	0.98
11	1	0	0	0	0	0.93	0.04	0.03	0	0.01	0.98	0.01	0	0.03	0.01	0.96
12	1	0	0	0	0	0.96	0.03	0.01	0	0.01	0.99	0	0	0.02	0.03	0.95
13	1	0	0	0	0	0.97	0.01	0.02	0	0.02	0.96	0.03	0	0.01	0.02	0.97
14	1	0	0	0	0	0.97	0.01	0.02	0	0.02	0.97	0.01	0	0.01	0.03	0.96
15	1	0	0	0	0	0.95	0.03	0.02	0	0.02	0.95	0.03	0	0.06	0.02	0.92
16	1	0	0	0	0	0.98	0.01	0.01	0	0	0.98	0.02	0	0.03	0.04	0.93
17	1	0	0	0	0	0.96	0.01	0.03	0	0.03	0.96	0.01	0	0.03	0.02	0.95
18	1	0	0	0	0	0.97	0.02	0.01	0	0.01	0.97	0.02	0	0.03	0.01	0.96
19	1	0	0	0	0	0.93	0.03	0.04	0	0.02	0.96	0.02	0	0.03	0.04	0.93
20	1	0	0	0	0	0.96	0.01	0.03	0	0.03	0.93	0.04	0	0.03	0.05	0.92