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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/24410

Title: A New Fault Diagnosis Method of Rotating Machinery
Authors: Chih-Hao Chen;Rong-Juin Shyu;Chih-Kao Ma
Contributors: NTOU:Department of Systems Engineering and Naval Architecture
Keywords: Fault diagnosis;rotating machinery;wavelet packets;fractal;box counting dimension;radial basis function neural network
Date: 2007
Issue Date: 2011-10-20T08:12:13Z
Publisher: Shock and Vibration
Abstract: Abstract:This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The faults of rotating machinery considered in this study include imbalance, misalignment, looseness and imbalance combined with misalignment conditions. When such faults occur, they usually induce non-stationary vibrations to the machine. After measuring the vibration signals, the wavelet packets transform is applied to these signals. The fractal dimension of each frequency bands is extracted and the box counting dimension is used to depict the failure characteristics of the vibration signals. The failure modes are then classified by a radial basis function neural network. An experimental study was performed to evaluate the proposed method and the results show that the method can effectively detect and recognize different kinds of faults of rotating machinery.
Relation: 15(6), pp.585-598
URI: http://ntour.ntou.edu.tw/handle/987654321/24410
Appears in Collections:[系統工程暨造船學系] 期刊論文

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