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An intelligent method for condition diagnosis of a pump system is proposed using the discrete wavelet transform (DWT), rough sets (RS), and a neural network to detect faults and distinguish fault types at an early stage. The Daubechies wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across an optimum frequency region. We also propose a new diagnosis method based on a fuzzy neural network realized by the partially-linearized neural network (PNN), one which can automatically distinguish fault types on the basis of the probability distributions of symptom parameters. The diagnosis knowledge for the training of the PNN can be acquired by using the RS. The PNN can deal with the ambiguity problem of diagnosis, and is always convergent. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.