Diagnosis method of centrifugal pumps by Rough Sets and Partially-linearized Neural Network

When using neural network (N.N.) for automatic diagnosis, it is difficult to deal with the ambiguous diagnosis problems. This paper proposes the "Partially-leanrized Neural Network (P.M.N.)" by which failure types can be quickly distinguished on the basis of the probability distributions of symptom parameters. The knowledge acquisition method for the P.N.N learning by using the Rough Sets is also proposed We have applied these method to the falure diagnosis of cetrifugal pumps which are used in a chemical plant The results of the failure diagnosis have verified that the methods are effective. The methods discussed here can also be applied to other diagnosis or pattern recognition problems.