A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

By Haidong Shao, Hongkai Jiang, Yingying Lin, Xingqiu Li. 2018

434 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/bfca19beea1acd7405a8202ec9e0d5f09d728c41

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

In Mechanical Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

Citations in the corpus

• Ensemble deep learning in bioinformatics (2020, methodology): “An example extending this approach uses different activation functions and a weighting scheme to improve model accuracy 40 .”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “In [127], an ensemble deep auto-encoder consisting of a series of auto-encoders (AE) based on different activation functions is proposed for unsupervised feature learning from the measured vibration signal.”
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, background): “[14] proposed a transfer learning based approach for fault diagnosis.”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “An ensemble deep auto-encoders (EDAE) is employed in [132] with a series of auto-encoders (AE) with different activation functions for unsupervised feature learning from the measured vibration signals.”