摘要：In this paper the failure sets and symptom sets of the problem for a 1000MW unit were determined. On the basis of distinguishing the precipitous decline and slow decline of vacuum, the calculation model of the state quantization value of every symptom parameter was established and the fault characteristic vector of the lower vacuum of the condenser was obtained by the simulation test of the unit. Based on BP neural network, the fault diagnosis model of condenser was established, and the low vacuum fault of the unit was diagnosed. The results show that the fault diagnosis of condensers can be used in the actual unit operation according to the fault theory domain feature vector of 1000MW unit.
