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The developed feature vector can serve better for planetary gearbox fault classification.
Current fault classification generally depends on feature pattern difference of different fault classes.
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.
A top classifier followed by a back propagation process is used for fault classification.
Other works on more advanced signal analysis and superior fault classification approaches were also discussed.
The DGA is exploited for fault classification tools implementation using the artificial intelligence techniques.
The scheme is constituted by two components: residual generation and fault classification.
Parallel implementation of these two GLR charts aids with fault classification as well.
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