Bearing Fault Diagnosis Using Structure Optimized Deep Convolutional Neural Network under Noisy Environment

• Published in: IOP conference series. Materials Science and Engineering Vol. 630; no. 1; pp. 12018 - 12022
• Main Authors: Jin, Guoqiang, Li, Dongjie, Wei, Ye, Hou, Wenhui, Chen, Huai'an, Jin, Yi, Zhu, Chang'an
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.10.2019
• Subjects: Artificial neural networks; Deep learning; Fault diagnosis; Feature extraction; Machine learning; Neural networks; Parameters; Representations

In recent years, deep learning has been gradually applied in bearing fault diagnosis thanks to its powerful learning and representation ability. However, deep learning-based methods are usually computationally intensive and have redundant parameters. In this paper, we propose an end-to-end structure...