A fault set and a symptom set were established in order to exactly judge and to quickly dispose in turbine startup of a power plant. There are ten typical faults in the fault set and sixteen fault symptoms in the symptom set. In consideration of the various kinds of change directions and ranges of the fault symptom parameters, the fuzzy disposal of nine degrees is put forward to build a set of typical fault-character-sample mode. A neural network model for fault diagnosis was obtained by fuzzy theory and radial basis function, and it was validated by using evaluator. It shows that the fuzzy fault disposal and the swiftness of training constringency are very satisfied in turbine startup of this power plant.

This content is only available via PDF.
You do not currently have access to this content.