A transfer convolutional neural network for fault diagnosis based on ResNet-50

By Long Wen, Xinyu Li, Liang Gao. Neural computing & applications (Print), 2019

677 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/b917054d4af5fd52ef4ee088c6b1e321d55fd29a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Dhiraj Neupane
2. Jong-Hoon Seok
3. Gunasekaran Manogaran
4. Yihao Xue
5. Mengjie Huang
6. Min Xia
7. Lei Shu
8. Clarence W. de Silva

In Mechanics of Materials

1. Dhiraj Neupane
2. Yihao Xue
3. Mengjie Huang
4. Min Xia
5. Lei Shu
6. Jong-Hoon Seok
7. Clarence W. de Silva
8. Rui Yang

Citations in the corpus

• A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic (2020, positive): “According to [20,21], ResNet-50 has achieved better results when it is used as a feature extractor.”
• Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning (2021, background): “Wen et al. [16] presented a transfer CNN for fault diagnosis based on ResNet-50, which achieved accurate diagnosis results on bearing faults and self-priming centrifugal pump faults.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “Similarly, a transfer CNN for fault diagnosis based on ResNet-50 is proposed for fault diagnosis of bearings in [48] in which they used transfer CNN (TCNN) with the depth of 51 convolutional layers.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “[81], [82], [83] or RGB images [79], [84], researchers applied the pretrained CNN structure using large-scale image datasets,”