Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network

Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to...

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Bibliographic Details
Main Authors: Zhunan Shen, Xiangwei Kong, Liu Cheng, Rengen Wang, Yunpeng Zhu
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Sensors
Subjects:
rolling bearing
intelligent fault diagnosis
adversarial generative network
multi-task learning
semi-supervised learning
Online Access:https://www.mdpi.com/1424-8220/24/4/1290

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