ROTOR FAULT
DIAGNOSIS METHOD BASED
ON KERNEL FUNCTION APPROXIMATION
LI Weihua1 SHI Tielin2
YANG Shuzi2
(1. School of Automotive Engineering,
South China University of Technology, Guangzhou 510640;
2. School of Mechanical Science and
Engineering, Huazhong University of Science and Technology, Wuhan 430074
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Abstract: Kernel
function approximation is investigated together with some applications
in mechanical fault diagnosis, and an approach to rotor fault
classification based on feature samples selection is presented. The
integral operator kernel functions is used to realize the nonlinear map
from the raw feature space of rotor vibration signals to high
dimensional feature space, where appropriate feature samples are
selected to classify three kinds of rotor faults: rotor crack, rotor
unbalance and rotor rub. The quantity of selected samples is much less
than that of whole sample sets, which has quickly expedited the
computation process. The classification result of KFA is compared with
that of SVM. It can be seen that the classification accuracy of KFA is
fairly as well as that of SVM, and KFA is or even better than SVM in
terms of computation load.
Key words: Kernel
function Feature selection Fault classification Rotor
CLC No: TG156
国家重点基础研究973计划(2003CB716207), 国家自然科学基金(50475095), 广东省自然科学基金(05300143,04020082), 振动, 冲击与噪声国家重点实验室开放基金(VSN-2004-03)和广东省电动汽车重点实验室开放基金(E4060110)资助项目.
Received 20051108, received in revised form 20060407
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