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Abstract: An approach to gear fault diagnosis is presented, which
bases on kernel principal component analysis (KPCA). In this approach,
the integral operator kernel functions is used to realize the nonlinear
map from the raw feature space of gear vibration signals to high
dimensional feature space. By performing PCA on the high dimensional
feature sets, the nonlinear principal components of raw feature space
are obtained. In succession, the selected nonlinear principal components
are used to construct the feature subspace for classification of gearbox
working conditions. The experimental data sets of gearbox working under
three conditions: normal, tooth cracked and tooth broken are used to
test the KPCA based method. The classification effect of KPCA based
method is compared with that of PCA based method. The results indicate
that the method can perform gear crack detection efficiently and can
fulfill fault classification accurately, and it is more suitable for
nonlinear feature extraction from fault signals.
Key words: Fault diagnosis Pattern classification Feature
extraction Kernel principal component analysis
CLC No: TH17
TP18
国家重大基础研究专项基金(G1998020320)和湖北省自然科学基金(2000J125)资助项目. Received
20020711, received in revised form 20030120
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