Fault Diagnosis of Centrifugal Pump Using Symptom Parameters in Frequency Domain

This paper presents a fault diagnosis method for a centrifugal pump system with frequency-domain symptom parameters by using the wavelet transform, rough sets and a fuzzy neural network to detect faults and distinguish fault types at an early stage. The wavelet transform is used for feature extraction across an optimum frequency region. The diagnosis knowledge for the training of neural network can be acquired by rough sets. A fuzzy neural network called “partially-linearized neural network” is proposed, by which the fault types of machinery can be quickly and effectively distinguished on the basis of the possibility grades 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.