Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

By Meng Gan, Cong Wang, Chang'an Zhu. 2016

539 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/ee7666e9dc19420a49ec953ab324b603e4c51328

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. Jing Lin
2. Kaixuan Liang
3. Jinyang Jiao
4. Ming Zhao
5. Liang Gao
6. Xinyu Li
7. Long Wen
8. S. Qin

In Mechanics of Materials

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Ming Zhao
5. Jing Lin
6. Kaixuan Liang
7. Boyuan Yang
8. Yan Xu

Citations in the corpus

• Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions (2020, background): “Gan et al. proposed a hierarchical diagnosis network by collecting DBNs for the hierarchical identification of mechanical fault pattern recognition of rolling element bearings [17].”
• A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, background): “With this advantage, DL could avoid the effect of handcrafted features designed by engineers, and it has shown a good prospect on fault diagnosis [11], [12].”
• A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (2018, methodology): “[15] H. Shao, H. Jiang, F. Wang, and H. Zhao, “An enhancement deep feature fusion method for rotating machinery fault diagnosis,” Knowl.”
• Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox (2019, background): “Different deep models such as stacked denoising autoencoders [15], deep belief network [16], sparse filtering [17], convolutional neural networks (CNNs) [18], and recurrent neural network [19] have demonstrated state-of-the-art diagnosis performance com-”
• Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis (2017, background): “For instance, there exists obvious frequency amplitude/energy attribute/distribution among the nearby frequency points/nodes in the FFT spectrum [14], [15] and WP nodes [16].”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “The popular deep learning based diagnostic models include deep auto-encoder [11], deep belief network [12], recurrent neural network [13], and convolutional neural network (CNN) [14].”
• A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning (2019, background): “Many studies have taken advantage of DNN for mechanical health monitoring [22]–[28].”