A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples

A Rolling Bearing Fault Diagnosis Based on Conditional Depth Convolution Countermeasure Generation Networks under Small Samples

Aiming at the problems of low fault diagnosis accuracy caused by insufficient samples and unbalanced data sample distribution in bearing fault diagnosis, this paper proposes a fault diagnosis method for rolling bearings referencing conditional deep convolution adversarial generative networks (C−DCGA...

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Bibliographic Details
Main Authors: Cheng Peng, Shuting Zhang, Changyun Li
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Sensors
Subjects:
small sample
data augmentation
spectral normalization
fault diagnosis
Online Access:https://www.mdpi.com/1424-8220/22/15/5658

Internet

https://www.mdpi.com/1424-8220/22/15/5658

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