Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

By Feng Jia, Yaguo Lei, Jing Lin, Xin Zhou, Na Lu. 2016

1,612 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/7eb26438ca18093bff5d33e9b2c8b95e20ddd209

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. Dhiraj Neupane
6. Jong-Hoon Seok
7. Liang Gao
8. Xinyu Li

In Mechanics of Materials

1. Dhiraj Neupane
2. Jong-Hoon Seok
3. Yaguo Lei
4. Naipeng Li
5. Ningbo Li
6. Liang Guo
7. Shen Zhang
8. Bingnan Wang

Citations in the corpus

• 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 New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, background): “[25] investigated the deep neural network based on AE for intelligent fault diagnosis.”
• Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes (2018, background): “Deep belief networks (DBNs), deep auto-encoders (DAEs), and convolutional NNs (CNNs) are popular deep learning methods used for various fault diagnosis applications in recent years [7]–[14].”
• Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, methodology): “example, DNN is employed to deal with the raw data [36]–[39] and the transform domain data [40].”
• Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis (2017, methodology): “Second, when building the architecture of CNN, we also followed the simple rule in [14].”
• Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, background): “To tackle these issues, an adaptive feature learning based diagnostic framework with deep learning technology is emerging in the second stage [11]-[14].”
• Pavement distress detection and classification based on YOLO network (2020, background): “In addition, CNN had strong back-to-back noise filtering capabilities (Jia et al. 2016, Mansanet et al. 2016), which was sufficient to overcome the spot noise and low comparability issues in the pavement inspection images.”
• Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions (2017, positive): “[9] used deep neural networks to recognize faults by training the model with frequency spectra extracted from vibration signals.”