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The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification.
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Fig. 6 Fault Classification Algorithm.
Fig. 2 Procedure for fault classification.
PVV developed the Fourier analysis for fault classification methodology.
Fig. 6 depicts the fault classification algorithm in detail.
Current fault classification generally depends on feature pattern difference of different fault classes.
The developed feature vector can serve better for planetary gearbox fault classification.
It is critical to extract features with enough health status information for fault classification.
After that, a generalized regression neural network (GRNN) is used for fault classification.
Observations in the orbit, spectra and fault classification diagrams are consistent with the previous experimental case.
Analyzed results show that the proposed method is effective for REB incipient weak fault classification.
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