Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation

By Xiang Li, Qian Ding, Jianqiao Sun. Journal of Intelligent Manufacturing, 2018

367 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/de87ed327b1ac2ceeb5a206a8695811ec8d982bb

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. Chuang Sun
2. Xuefeng Chen
3. Ming Zhao
4. Ruqiang Yan
5. Jing Lin
6. Kaixuan Liang
7. Jinyang Jiao
8. Dhiraj Neupane

In Mechanical Engineering

1. Dhiraj Neupane
2. Ming Zhao
3. Jing Lin
4. Shibin Wang
5. Jong-Hoon Seok
6. Chuang Sun
7. Kaixuan Liang
8. Ruqiang Yan

In Mechanics of Materials

1. Xuefeng Chen
2. Ruqiang Yan
3. Chuang Sun
4. Dhiraj Neupane
5. Ming Zhao
6. Jing Lin
7. Jong-Hoon Seok
8. Kaixuan Liang

Citations in the corpus

• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “Multiple wavelet regularizations [55], data augmentation methods [56], and information fusion technology [57] were also applied to improve the performance of CNN models.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “An intelligent rotating machinery fault diagnosis based on DL using data augmentation technique is proposed in [90].”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, positive): “osed the multiple wavelet coefficients fusion based deep residual network for planetary gearbox fault diagnosis, which aimed to learn more easily-distinguished features from the input data. Li et al. [142] proposed a deep residual learning network for fault diagnosis, in which the data augmentation ”