Research Article
Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network
Table 8
Fault diagnosis results of polymerizer.
| Test sample | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| BP output | 0.9988 | 0.0000 | 0.0000 | 0.0016 | 0.0012 | 0.0000 | 0.0004 | 0.0018 | 0.0000 | 0.0012 | 0.0039 | 0.0000 | 0.0002 | 0.0122 | 0.9833 | 0.0001 | 0.9848 | 0.0132 | 0.0002 | 0.0000 | 0.0004 | 0.0011 | 0.0006 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.9837 | 0.0008 | 0.0010 | 0.0026 | 0.9986 | 0.0019 | 0.0002 | 0.0033 | 0.9978 | 0.0002 | 0.0000 |
| Real code | 1000 | 0000 | 0000 | 0001 | 0100 | 0000 | 0100 | 0001 | 0000 | 0010 |
| 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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