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Separating sensitive characteristic signals from original vibration data is an important challenge for rolling bearing fault diagnosis. Because it is difficult to obtain large number of damaged bearings, Rolling bearing fault datasets are often small sample datasets. For the classification of small sample rolling bearing fault datasets, we propose a coupling vibration data classification method based on triplet embedding. The method is divided into two steps feature extraction and fault identification. First, build a triple embedding ba
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