Exact(60)
This phenomenon is an early bearing fault feature.
The improved MF can obtain the fault feature from low SNR signals.
In this study, we develop a novel fault feature vector based on the dependence.
These provide a new method for a gear system fault feature extraction and classification.
Especially when early gear fault occurs, the weak fault feature information is submerged in interfering signal.
Particularly, the weaker fault feature signal is generally submerged by the stronger one and background noise.
After splicing together all the reconstructed signals, the fault feature is extracted successfully.
TEK further improves the effectiveness of ACDIF for fault feature extraction.
Then, fault feature samples are divided into train samples and test samples.
It is of great value to detect the resulting fault feature automatically.
One limitation associated with these methods is the empirical knowledge required for fault feature selection.
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