Abstract
This paper proposes an intelligent diagnosis method for a centrifugal pump system with frequency-domain symptom parameters using wavelet Transform, rough sets and fuzzy neural network to detect faults and distinguish fault types at an early stage. The wavelet transform is used to extract fault features to capture the fault information hidden in signals. The diagnosis knowledge for the training of neural network can be acquired by rough sets. A fuzzy neural network called "partially-linearized neural network (PNN)" is proposed, by which the fault types of machinery can be quickly and effectively distinguished on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters that can reflect the characteristics of signals are also described in frequency-domain. Practical examples of diagnosis for a centrifugal pump system are shown to verify the efficiency of the method.
