Affiliations: Department of System Engineering and Naval
Architecture, National Taiwan Ocean University, Keeling, Taiwan | Graduate school of Gerontic Technology and Service
Management, Nan Kai Institute of Technology, Tianjin, Taiwan
Note: [] Corresponding author: Chih-Hao Chen, Department of System
Engineering and Naval Architecture, National Taiwan Ocean University, 2
Pei-Ning Road, Keelung 20224, Taiwan. Tel.: +886 2246 22192 ext. 6033; Fax:
+886 2246 24634; E-mail: [email protected]
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.
