Intelligent diagnosis method for a centrifugal pump using features of vibration signals |
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Authors: | Huaqing Wang Peng Chen |
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Affiliation: | (1) Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu Mie, 5148507, Japan;(2) School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, 15 BeiSanhuan East Road, 100029 Beijing, ChaoYang District, China |
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Abstract: | In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because
the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because
definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis
method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived
using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used
to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency
regions. As the input parameters for the neural network, the non-dimensional symptom parameters that can reflect the characteristics
of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using
the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition
diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically.
The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical
examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method. |
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Keywords: | Intelligent diagnosis Neural network Rough sets Wavelet transform Vibration signal Centrifugal pump |
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