Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

By Haidong Shao, Hongkai Jiang, Haizhou Zhang, Wenjing Duan, Tianchen Liang, Shuaipeng Wu. 2018

349 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/155e93d0e4d1939d9d8072906d627f19439fd1ec

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. Ming Zhao
2. Jing Lin
3. Kaixuan Liang
4. Jinyang Jiao

In Mechanical Engineering

1. Ming Zhao
2. Jing Lin
3. Kaixuan Liang
4. Jinyang Jiao

Citations in the corpus

• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “[43] proposed a bearing fault diagnosis method based on the deep convolutional belief network and the compressed sensing technique.”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, neutral): “Other examples include a mixture of CNN and DBN [163], a deep residual network (DRN) [164], [165], a deep stack network [166], a RNN based auto-encoders [167], sparse filtering [168], and a ew deep le rning architecture – the capsule network [169], proposed by Hinton et al.”