Title :
Shape Feature and RBF Network Based Intelligent Fault Identification of Rotating Machinery
Author :
Wang, Changqing ; Zhou, Jianzhong ; Zhang, Xiaoyuan ; Yang, Mengqi ; Zhang, Yongchuan
Abstract :
The shape of shaft orbit reflects the working state of rotating machinery. It plays an important role in the fault identification of water turbine generator set. This paper is mainly focused on using chain codes technique and Radial basis function (RBF) network to perform intelligent identification of different shaft orbit generated by different fault. Chain code is a contour-based representation for shaft orbit. It has properties of simple calculation, low storage requirement and translation invariant. And it is used as the feature of shaft orbit. In succession, the features are input to RBF network to identify the shaft orbit for fault identification. The experimental result indicates the proposed approach is very effective and has satisfactory accuracy.
Keywords :
condition monitoring; fault diagnosis; feature extraction; hydraulic turbines; mechanical engineering computing; radial basis function networks; RBF network; intelligent fault identification; radial basis function network; rotating machinery; shaft orbit; shape feature extraction; water turbine generator; Fault diagnosis; Feature extraction; Orbits; Radial basis function networks; Shafts; Shape; Training; Radial basis function network; chain code; fault identification; shaft orbit;
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
DOI :
10.1109/GCIS.2010.73