A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

By Feng Jia, Yaguo Lei, Liang Guo, Jing Lin, Saibo Xing. Neurocomputing, 2018

491 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/b488561b64aaf4deb66f3b3e3c1a2f7724bcc43d

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. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler
5. Liang Gao
6. Xinyu Li
7. Long Wen
8. Yaguo Lei

In Mechanical Engineering

1. Naipeng Li
2. Shen Zhang
3. Bingnan Wang
4. Shibo Zhang
5. Thomas G. Habetler
6. Yaguo Lei
7. Liang Gao
8. Xinyu Li

In Mechanics of Materials

1. Naipeng Li
2. Yaguo Lei
3. Liang Guo
4. Shen Zhang
5. Bingnan Wang
6. Shibo Zhang
7. Thomas G. Habetler
8. Tao Yan

Citations in the corpus

• Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, background): “INTELLIGENT fault diagnosis is able to handle massive monitoring data and distinguish health conditions of machines [1]–[3].”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “47% [131] SAE-local connection network 2 Softmax Shift-invariant features 25% 99.”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “47% [130] SAE-local connection network 2 Softmax Shift-invariant features 25% 99.”
• A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, methodology): “Jia et al. [23] proposed normalized SAE with local connection network (NSAE-LCN) for intelligent fault diagnosis of machines and NSAE-LCN outperformed the commonly used diagnosis network.”