Research Article
Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network
Table 10
Fault diagnosis results.
| Test sample | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| BP output | 0.9957 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0007 | 0.0000 | 0.0033 | 0.0174 | 0.0162 | 0.9767 | 0.0008 | 0.9604 | 0.0173 | 0.0012 | 0.0000 | 0.0001 | 0.0210 | 0.0006 | 0.0000 | 0.0000 | 0.0001 | 0.0000 | 0.0000 | 0.0000 | 0.9993 | 0.0000 | 0.0000 | 0.0036 | 0.9998 | 0.0000 | 0.0000 | 0.0001 | 0.9965 | 0.0000 | 0.0000 |
| Diagnosis type | Gland-shaft fault | Normal | Normal | Motor fault | Mechanical seal fault | Normal | Gland-shaft fault | Motor fault | Normal | Reducer fault |
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The bold values represent the fault diagnosis results of BP neural network by using test samples.
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