Table 6 CNN2 Confusion report
From: Multi combination pattern labeling by using deep learning for chameleon rotary machine environment
Precision | Recall | f1-score | Support | |
|---|---|---|---|---|
Axis_11 | 1.00 | 0.00 | 0.01 | 300 |
Axis_22 | 0.45 | 0.35 | 0.39 | 300 |
Axis_2d2 | 0.39 | 0.11 | 0.18 | 300 |
Axis_30 | 0.47 | 0.65 | 0.54 | 300 |
Axis_37 | 1.00 | 1.00 | 1.00 | 300 |
Axis_3d75 | 0.49 | 0.97 | 0.65 | 300 |
Axis_3d7 | 0.48 | 0.90 | 0.36 | 300 |
Axis_5d5 | 0.48 | 0.75 | 0.59 | 300 |
Axis_7d5 | 0.00 | 0.00 | 0.00 | 300 |
Bearing_11 | 1.00 | 1.00 | 1.00 | 300 |
Bearing_15 | 1.00 | 1.00 | 1.00 | 300 |
Bearing_18d5 | 1.00 | 1.00 | 1.00 | 300 |
Bearing_2d2 | 1.00 | 1.00 | 1.00 | 300 |
Bearing_3d7 | 1.00 | 1.00 | 1.00 | 300 |
Bearing_5d5 | 1.00 | 1.00 | 1.00 | 300 |
Bearing_7d5 | 1.00 | 1.00 | 1.00 | 300 |
Belt_11 | 0.44 | 0.20 | 0.27 | 300 |
Belt_15 | 0.50 | 1.00 | 0.67 | 300 |
Belt_18d5 | 0.50 | 1.00 | 0.67 | 300 |
Belt_22 | 0.47 | 0.57 | 0.51 | 300 |
Belt_2d2 | 0.48 | 0.82 | 0.61 | 300 |
Belt_55 | 0.42 | 0.25 | 0.32 | 300 |
Belt_5d5 | 0.23 | 0.01 | 0.02 | 300 |
Belt_7d5 | 0.26 | 0.03 | 0.06 | 300 |
Normal_11 | 1.00 | 1.00 | 1.00 | 300 |
Normal_15 | 0.99 | 1.00 | 0.99 | 300 |
Normal_18d5 | 1.00 | 1.00 | 1.00 | 300 |
Normal_22 | 1.00 | 1.00 | 1.00 | 300 |
Normal_2d2 | 1.00 | 1.00 | 1.00 | 300 |
Normal_30 | 1.00 | 1.00 | 1.00 | 300 |
Normal_37 | 1.00 | 1.00 | 1.00 | 300 |
Normal_3d75 | 1.00 | 1.00 | 1.00 | 300 |
Normal_3d7 | 1.00 | 1.00 | 1.00 | 300 |
Normal_55 | 1.00 | 1.00 | 1.00 | 300 |
Normal_5d5 | 1.00 | 1.00 | 1.00 | 300 |
Normal_7d5 | 1.00 | 1.00 | 1.00 | 300 |
Rotating_11 | 1.00 | 1.00 | 1.00 | 300 |
Rotating_15 | 1.00 | 0.99 | 0.99 | 300 |
Rotating_22 | 1.00 | 1.00 | 1.00 | 300 |
Rotating_2d2 | 1.00 | 1.00 | 1.00 | 300 |
Rotating_3d7 | 1.00 | 1.00 | 1.00 | 300 |
Rotating_55 | 1.00 | 1.00 | 1.00 | 300 |
Rotating_5d5 | 1.00 | 1.00 | 1.00 | 300 |
Accuracy | 0.81 | 12898 | ||
Macro avg | 0.79 | 0.81 | 0.77 | 12898 |
Weighted avg | 0.79 | 0.81 | 0.77 | 12898 |